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UNIT-I
Syllabus :
Introduction : What Motivated Data Mining? Why Is It Important, Data Mining—On What Kind
of Data, Data Mining Functionalities—What Kinds of Patterns Can Be Mined? Are All of the
Patterns Interesting? Classification of Data Mining Systems, Data Mining Task Primitives,
Integration of a Data Mining System with a Database or Data Warehouse System, Major Issues
in Data Mining. (Han & Kamber)

1.1 What motivated data mining? Why is it important?

The major reason that data mining has attracted a great deal of attention in information
industry in recent years is due to the wide availability of huge amounts of data and the imminent
need for turning such data into useful information and knowledge.
 The information and knowledge gained can be used for applications ranging from business
management, production control, and market analysis, to engineering design and science
exploration.
The evolution of database system technology

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1.2 What is data mining

 Data mining refers to extracting or mining" knowledge from large amounts of data.
 There are many other terms related to data mining, such as knowledge mining, knowledge
extraction, data/pattern analysis, data archaeology, and data dredging.
 Many people treat data mining as a synonym for another popularly used term, Knowledge
Discovery in Databases", or KDD

Essential step in the process of knowledge discovery in databases

Knowledge discovery as a process is depicted in following figure and consists of an iterative
sequence of the following steps:
 data cleaning: to remove noise or irrelevant data
 data integration: where multiple data sources may be combined
 data selection: where data relevant to the analysis task are retrieved from the database
 data transformation: where data are transformed or consolidated into forms appropriate
for mining by performing summary or aggregation operations
 data mining :an essential process where intelligent methods are applied in order to extract
data patterns
 pattern evaluation to identify the truly interesting patterns representing knowledge based
on some interestingness measures
 knowledge presentation: where visualization and knowledge representation techniques
are used to present the mined knowledge to the user.

Data mining as a step in the process of knowledge discovery.

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Architecture of a typical data mining system/Major Components

Data mining is the process of discovering interesting knowledge from large amounts of
data stored either in databases, data warehouses, or other information repositories.
 Based on this view, the architecture of a typical data mining system may have the following
major components:
1. A database, data warehouse, or other information repository, which consists of the set
of databases, data warehouses, spreadsheets, or other kinds of information repositories
containing the student and course information.
2. A database or data warehouse server which fetches the relevant data based on users’
data mining requests.
3. A knowledge base that contains the domain knowledge used to guide the search or to
evaluate the interestingness of resulting patterns. For example, the knowledge base
may contain metadata which describes data from multiple heterogeneous sources.
4. A data mining engine, which consists of a set of functional modules for tasks such as
classification, association, classification, cluster analysis, and evolution and deviation
analysis.
5. A pattern evaluation module that works in tandem with the data mining modules by
employing interestingness measures to help focus the search towards interestingness
patterns.
6. A graphical user interface that allows the user an interactive approach to the data
mining system.

Architecture of a typical Data mining system

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How is a data warehouse different from a database? How are they similar?
 Differences between a data warehouse and a database:
 A data warehouse is a repository of information collected from multiple sources, over a
history of time, stored under a unified schema, and used for data analysis and decision
support; whereas a database, is a collection of interrelated data that represents the current
status of the stored data.
 There could be multiple heterogeneous databases where the schema of one database may
not agree with the schema of another.
 A database system supports ad-hoc query and on-line transaction processing
 Similarities between a data warehouse and a database: Both are repositories of
information, storing huge amounts of persistent data.

1.2 Data mining: on what kind of data? / Describe the following advanced database systems
and applications: object-relational databases, spatial databases, text databases, multimedia
databases, the World Wide Web.
 In principle, data mining should be applicable to any kind of information repository.
 This includes relational databases, data warehouses, transactional databases, advanced
database systems, flat files, and the World-Wide Web.
 Advanced database systems include object-oriented and object-relational databases, and
special c application-oriented databases, such as spatial databases, time-series databases, text
databases, and multimedia databases.

Flat files:
Flat files are actually the most common data source for data mining algorithms, especially
at the research level.
 Flat files are simple data files in text or binary format with a structure known by the data
mining algorithm to be applied.
 The data in these files can be transactions, time-series data, scientific measurements, etc.

Relational Databases:
A relational database consists of a set of tables containing either values of entity attributes,
or values of attributes from entity relationships.
 Tables have columns and rows, where columns represent attributes and rows represent tuples.
 A tuple in a relational table corresponds to either an object or a relationship between objects
and is identified by a set of attribute values representing a unique key.
 In following figure it presents some relations Customer, Items, and Borrow representing
business activity in a video store.
 These relations are just a subset of what could be a database for the video store and is given
as an example.

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 The most commonly used query language for relational database is SQL, which allows
retrieval and manipulation of the data stored in the tables, as well as the calculation of
aggregate functions such as average, sum, min, max and count.
 For instance, an SQL query to select the videos grouped by category would be:
SELECT count(*) FROM Items WHERE type=video GROUP BY category.
 Data mining algorithms using relational databases can be more versatile than data mining
algorithms specifically written for flat files, since they can take advantage of the structure
inherent to relational databases.
 While data mining can benefit from SQL for data selection, transformation and consolidation,
it goes beyond what SQL could provide, such as predicting, comparing, detecting deviations,
etc.

Data warehouses
A data warehouse is a repository of information collected from multiple sources, stored
under a unified schema, and which usually resides at a single site.
 Data warehouses are constructed via a process of data cleansing, data transformation, data
integration, data loading, and periodic data refreshing.
 The figure shows the basic architecture of a data warehouse

Typical framework of a data warehouse for AllElectronics

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 In order to facilitate decision making, the data in a data warehouse are organized around major
subjects, such as customer, item, supplier, and activity.
 The data are stored to provide information from a historical perspective and are typically
summarized.
 A data warehouse is usually modeled by a multidimensional database structure, where each
dimension corresponds to an attribute or a set of attributes in the schema, and each cell stores
the value of some aggregate measure, such as count or sales amount.
 The actual physical structure of a data warehouse may be a relational data store or a
multidimensional data cube.
 It provides a multidimensional view of data and allows the precomputation and fast accessing
of summarized data.

A multidimensional data cube, commonly used for data warehousing, (a) showing summarized
data for AllElectronics and (b) showing summarized data resulting fromdrill-down and roll-up
operations on the cube in (a). For improved readability, only some of the cube cell values are
shown.

 The data cube structure that stores the primitive or lowest level of information is called a base
cuboid.
 Its corresponding higher level multidimensional (cube) structures are called (non-base)
cuboids.
 A base cuboid together with all of its corresponding higher level cuboids form a data cube.
 By providing multidimensional data views and the precomputation of summarized data, data
warehouse systems are well suited for On-Line Analytical Processing, or OLAP.
 OLAP operations make use of background knowledge regarding the domain of the data being
studied in order to allow the presentation of data at different levels of abstraction. Such
operations accommodate different user viewpoints.
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 Examples of OLAP operations include drill-down and roll-up, which allow the user to view
the data at differing degrees of summarization, as illustrated in above figure.

Transactional databases
In general, a transactional database consists of a flat file where each record represents a
transaction.
 A transaction typically includes a unique transaction identity number (trans ID), and a list of
the items making up the transaction (such as items purchased in a store) as shown below:

Fragement of a transactional database for sales at AllElectronics

Advanced database systems and advanced database applications

An objected-oriented database is designed based on the object-oriented programming
paradigm where data are a large number of objects organized into classes and class hierarchies.
 Each entity in the database is considered as an object.
 The object contains a set of variables that describe the object, a set of messages that the object
can use to communicate with other objects or with the rest of the database system and a set of
methods where each method holds the code to implement a message.

Spatial database
A spatial database contains spatial-related data, which may be represented in the form of
raster or vector data.
 Raster data consists of n-dimensional bit maps or pixel maps, and vector data are represented
by lines, points, polygons or other kinds of processed primitives, Some examples of spatial
databases include geographical (map) databases, VLSI chip designs, and medical and satellite
images databases.
 Ex : 2-D satellite images may be represented as raster format .
Maps can be represented in Vector format .

Time – Series Databases :

Time-Series Databases: Time-series databases contain time related data such stock
market data or logged activities.
 These databases usually have a continuous flow of new data coming in, which sometimes
causes the need for a challenging real time analysis.
 Data mining in such databases commonly includes the study of trends and correlations
between evolutions of different variables, as well as the prediction of trends and movements
of the variables in time.

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Text database
A text database is a database that contains text documents or other word descriptions in
the form of long sentences or paragraphs, such as product specifications, error or bug reports,
warning messages, summary reports, notes, or other documents.

Multimedia database
A multimedia database stores images, audio, and video data, and is used in applications
such as picture content-based retrieval, voice-mail systems, video-on-demand systems, the World
Wide Web, and speech-based user interfaces.

World- Wide Web

The World-Wide Web provides rich, world-wide, on-line information services, where
data objects are linked together to facilitate interactive access.
 Some examples of distributed information services associated with the World-Wide Web
include America Online, Yahoo!, AltaVista, and Prodigy.

1.3 Data mining functionalities/Data mining tasks: what kinds of patterns can be mined?
Data mining functionalities are used to specify the kind of patterns to be found in data
mining tasks. In general, data mining tasks can be classified into two categories:
• Descriptive
• predictive
Descriptive mining tasks characterize the general properties of the data in the database.
Predictive mining tasks perform inference on the current data in order to make predictions.

Describe data mining functionalities, and the kinds of patterns they can discover
(or)
Define each of the following data mining functionalities: characterization, discrimination,
association and correlation analysis, classification, prediction, clustering, and evolution analysis.
Give examples of each data mining functionality, using a real-life database that you are familiar
with.

1 .Concept/class description: characterization and discrimination

Data can be associated with classes or concepts. It describes a given set of data in a
concise and summarative manner, presenting interesting general properties of the data.
These descriptions can be derived via
1. data characterization, by summarizing the data of the class under study (often
called the target class) in general terms .
2. data discrimination, by comparison of the target class with one or a set of
comparative classes
3. both data characterization and discrimination
Data characterization
It is a summarization of the general characteristics or features of a target class of data.

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Example:
A data mining system should be able to produce a description summarizing the
characteristics of a student who has obtained more than 75% in every semester; the result could
be a general profile of the student.

The output of data characterization can be presented in various forms like

 Pie charts
 Bar charts
 Curves
 Multimedimensional cubes
 Multimedimensional tables etc.

The resulting descriptions can be presented as generalized relations or in rule forms called
characteristic rules.
Data Discrimination is a comparison of the general features of target class data objects
with the general features of objects from one or a set of contrasting classes.

Example
The general features of students with high GPA’s may be compared with the general
features of students with low GPA’s. The resulting description could be a general comparative
profile of the students such as 75% of the students with high GPA’s are fourth-year computing
science students while 65% of the students with low GPA’s are not.
Discrimination descriptions expressed in rule form are referred to as discriminant rules.

2 . Mining Frequent patterns , Associations and correlations .

Frequent patterns, as the name suggests, are patterns that occur frequently in data.
 There are many kinds of frequent patterns, including frequent itemsets, frequent subsequences
(also known as sequential patterns), and frequent substructures.
 A frequent itemset typically refers to a set of items that often appear together in a transactional
data set—for example, milk and bread, which are frequently bought together in grocery stores
by many customers.
 A frequently occurring subsequence, such as the pattern that customers, tend to purchase first
a laptop, followed by a digital camera, and then a memory card, is a (frequent) sequential
pattern.
 A substructure can refer to different structural forms (e.g., graphs, trees, or lattices) that may
be combined with itemsets or subsequences. If a substructure occurs frequently, it is called a
(frequent) structured pattern.
 Mining frequent patterns leads to the discovery of interesting associations and correlations
within data.

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It is the discovery of association rules showing attribute-value conditions that occur
frequently together in a given set of data. For example, a data mining system may find association
rules like
major(X, “computing science””) ⇒owns(X, “personal computer”)
[support = 12%, confidence = 98%]
where X is a variable representing a student. The rule indicates that of the students under
study, 12% (support) major in computing science and own a personal computer. There is a 98%
probability (confidence, or certainty) that a student in this group owns a personal computer.
Example:
Correlation analysis
Correlation analysis is a technique use to measure the association between two variables.
 Correlation is degree or type of relationship b/w two or more quantities ( variables).
 A correlation coefficient (r) is a statistic used for measuring the strength of a supposed linear
association between two variables. Correlations range from -1.0 to +1.0 in value.
 A correlation coefficient of 1.0 indicates a perfect positive relationship in which two or more
variables fluctuate together ( one increases/decreases and other one also increases/decreases).
 A correlation coefficient of 0.0 indicates no relationship between the two variables. That is,
one cannot use the scores on one variable to tell anything about the scores on the second
variable.
 A correlation coefficient of -1.0 indicates a perfect negative relationship in which high values
of one variable increases and other decreases.

3 . Classification and prediction

Classification:
Classification:
 It predicts categorical class labels
 It classifies data (constructs a model) based on the training set and the values (class labels)
in a classifying attribute and uses it in classifying new data
 Typical Applications
 credit approval
 target marketing
 medical diagnosis
 treatment effectiveness analysis
 Classification can be defined as the process of finding a model (or function) that describes
and distinguishes data classes or concepts, for the purpose of being able to use the model to
predict the class of objects whose class label is unknown.
 The derived model is based on the analysis of a set of training data (i.e., data objects whose
class label is known).
Example:
An airport security screening station is used to determine if passengers are potential
terrorist or criminals. To do this, the face of each passenger is scanned and its basic
pattern(distance between eyes, size, and shape of mouth, head etc) is identified. This pattern is
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compared to entries in a database to see if it matches any patterns that are associated with known
offenders
 A classification model can be represented in various forms, such as
1) IF-THEN rules,
student ( class , "undergraduate") AND concentration ( level, "high") ==> class A
student (class ,"undergraduate") AND concentrtion (level,"low") ==> class B
student (class , "post graduate") ==> class C
2) Decision tree

3) Neural network ( mathematical formulae )

Prediction:
Find some missing or unavailable numerical data values rather than class labels referred
to as prediction.
 Although prediction may refer to both numerical prediction and class label prediction, it is
usually confined to numerical data value prediction and thus is distinct from classification.
 Prediction also encompasses the identification of distribution trends based on the available
data.
 Regression analysis is a statistical methodology that is most often used for numerical
prediction .
 Classification and prediction may need to be preceded by a relevance analysis , which
attempts to identify attributes that do not contribute to the classification or prediction process.
These attributes can then be excluded.

Example:
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Predicting flooding is difficult problem. One approach is uses monitors placed at various
points in the river. These monitors collect data relevant to flood prediction: water level, rain
amount, time, humidity etc. These water levels at a potential flooding point in the river can be
predicted based on the data collected by the sensors upriver from this point. The prediction must
be made with respect to the time the data were collected.

Classification vs. Prediction

Classification differs from prediction in that the former is to construct a set of models (or
functions) that describe and distinguish data class or concepts, whereas the latter is to predict
some missing or unavailable, and often numerical, data values.
Their similarity is that they are both tools for prediction: Classification is used for
predicting the class label of data objects and prediction is typically used for predicting missing
numerical data values.

4 . Clustering analysis
Clustering analyzes data objects without consulting a known class label.
 The objects are clustered or grouped based on the principle of maximizing the intraclass
similarity and minimizing the interclass similarity.
 Cluster of objects are formed so that objects within a cluster have high similarity to one
another , but are very dissimilar to objects in other clusters.
 Each cluster that is formed can be viewed as a class of objects.
Clustering can also facilitate taxonomy formation, that is, the organization of observations into a
hierarchy of classes that group similar events together as shown below:

A 2-D plot of customer data with respect to customer locations in a city, showing three data
clusters.
Classification vs. Clustering
 In general, in classification you have a set of predefined classes and want to know which class
a new object belongs to.
 Clustering tries to group a set of objects and find whether there is some relationship between
the objects.

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In the context of machine learning, classification is supervised learning and clustering is
unsupervised learning

5 . Outlier analysis:
A database may contain data objects that do not comply with general behavior or model
of data.
 These data objects are outliers. In other words, the data objects which do not fall within the
cluster will be called as outlier data objects.
 Noisy data or exceptional data are also called as outlier data. The analysis of outlier data is
referred to as outlier mining.
Example
Outlier analysis may uncover fraudulent usage of credit cards by detecting purchases of
extremely large amounts for a given account number in comparison to regular charges incurred
by the same account. Outlier values may also be detected with respect to the location and type of
purchase, or the purchase frequency.

6 . Data evolution analysis

It describes and models regularities or trends for objects whose behavior changes over
time.
 Although this may include characterization, discrimination, association, classification, or
clustering of time-related data, distinct features of such an analysis include time-series data
analysis, sequence or periodicity pattern matching, and similarity-based data analysis.
Example:
The data of result the last several years of a college would give an idea if quality of graduated
produced by it

What is the difference between discrimination and classification? Between characterization

and clustering? Between classification and prediction? For each of these pairs of tasks, how
are they similar?
Answer:
• Discrimination differs from classification in that the former refers to a comparison of the general
features of target class data objects with the general features of objects from one or a set of
contrasting classes, while the latter is the process of finding a set of models (or functions) that
describe and distinguish data classes or concepts for the purpose of being able to use the model
to predict the class of objects whose class label is unknown. Discrimination and classification are
similar in that they both deal with the analysis of class data objects.
• Characterization differs from clustering in that the former refers to a summarization of the
general characteristics or features of a target class of data while the latter deals with the analysis
of data objects without consulting a known class label. This pair of tasks is similar in that they
both deal with grouping together objects or data that are related or have high similarity in
comparison to one another.

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• Classification differs from prediction in that the former is the process of finding a set of models
(or functions) that describe and distinguish data class or concepts while the latter predicts missing
or unavailable, and often numerical, data values. This pair of tasks is similar in that they both are
tools for
Prediction: Classification is used for predicting the class label of data objects and prediction is
typically used for predicting missing numerical data values.

1.4 Are all of the patterns interesting? / What makes a pattern interesting?
A pattern is interesting if,
(1) It is easily understood by humans,
(2) Valid on new or test data with some degree of certainty,
(3) Potentially useful, and
(4) Novel.
A pattern is also interesting if it validates a hypothesis that the user sought to confirm. An
interesting pattern represents knowledge.

Several objective measures of pattern interestingness exist.

 These are based on the structure of discovered patterns and the statistics underlying them.
 An objective measure for association rules of the form X )Y is rule support, representing the
percentage of transactions from a transaction database that the given rule satisfies.
 This is taken to be the probability P(.X U Y), where X U[Y indicates that a transaction contains
both X and Y, that is, the union of itemsets X and Y.
 Another objective measure for association rules is confidence, which assesses the degree of
certainty of the detected association.
 This is taken to be the conditional probability P(Y/X), that is, the probability that a transaction
containing X also contains Y.

Subjective interestingness measures are based on user beliefs in the data.

 These measures find patterns interesting if the patterns are unexpected (contradicting a user’s
belief) or offer strategic information on which the user can act.
 Ex : Unexpectedness , novelty, actionability .. etc

1.5 Classification of Data Mining Systems

There are many data mining systems available or being developed. Some are specialized
systems dedicated to a given data source or are confined to limited data mining functionalities,
other are more versatile and comprehensive.
Data mining systems can be categorized according to various criteria among other
classification are the following:

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Database Statistics

Machine Data Mining

Visualization

Pattern Other
Algorithm
Data Mining: Confluence of Multiple Disciplines

· Classification according to the kinds of databases mined : The DM system can be classified
according to the kinds of databases mined . Database systems can be classified according to
different criteria
1. Data models involved such as relational database, object-oriented database, data warehouse,
transactional, etc.
2. The type of data handled such as spatial data, multimedia data, time-series data, text data,
World Wide Web, etc.
· Classification according to the kinds of knowledge mined : this classification categorizes
data mining systems based on the kind of knowledge they mine , that is based on data mining
functionalities, such as characterization, discrimination, association, classification, clustering,
etc. Some systems tend to be comprehensive systems offering several data mining functionalities
together.
· Classification according to the kinds of techniques utilized :
Data mining systems can be categorized according to the data mining techniques
employed .
These techniques can be described according to the degree of user interaction involved in
the data mining process such as query-driven systems, interactive exploratory systems, or
autonomous systems.
And also according to the methods of data analysis employed in the data mining such as
database oriented or data warehouse – oriented techniques , machine learning , statistics ,
visualization , pattern recognition , neural networks etc .
A comprehensive system would provide a wide variety of data mining techniques to fit
different situations and options, and offer different degrees of user interaction.
• Classification according to the application adapted :
Data mining systems can also be categorized according to the application they adapt .
Ex : Finance . telecommunications . DNA, Stock markets , e-mail …Etc .
Different applications often require the integration of application – specific methods .

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1.6 Data Mining Task Primitives
Five primitives for specifying a data mining task

• The set of Task-relevant data to be mined :

This primitive specifies the portion of the database or the set of data in which the user is
interested .
This includes the database attributes or data warehouse dimensions of internet .
• The kind of Knowledge type to be mined:
This primitive specifies the specific data mining function to be performed, such as
characterization, discrimination, association, classification, clustering, or evolution analysis.
• The Background knowledge to be used in the discovery process :
This primitive allows users to specify knowledge they have about the domain to be mined.
Such knowledge can be used to guide the knowledge discovery process and evaluate the patterns
that are found.
Ex : Concept hierarchies , which allows data to be mined at multiple levels of abstraction .
User beliefs regarding relationships in the data are another form of back ground knowledge .

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• The interestingness measure and thresholds for pattern evaluation :

This primitive allows users to specify functions that are used to separate uninteresting
patterns from knowledge and may be used to guide the mining process, as well as to evaluate the
discovered patterns. This allows the user to confine the number of uninteresting patterns returned
by the process, as a data mining process may generate a large number of patterns. Interestingness
measures can be specified for such pattern characteristics as simplicity, certainty, utility and
novelty.
• Visualization of discovered patterns:
This primitive refers to the form in which discovered patterns are to be displayed. In order
for data mining to be effective in conveying knowledge to users, data mining systems should be
able to display the discovered patterns in multiple forms such as rules, tables, cross tabs (cross-
tabulations), pie or bar charts, decision trees, cubes or other visual representations.

1.7 Integration of Data Mining System with a Databade or Datawarehouse System

A good system architecture will facilitate the data mining system to make best use of the
software environment , accomplish data mining tasks in an efficient and timely manner with other
information systems .
 Design a Data Mining System should be integrate or couple with a Database (DB ) system
and/ or Data warehouse (DW) system .
 If a DM system works as a stand –alone system or is embedded in an application program ,
There are no DB or DW systems with which it has to communicate . This scheme is called
No- coupling .
 There are different integration schemes . Those are
1. No – coupling
2. Loose coupling
3. Semitight –coupling
4. Tight – coupling

1. No- coupling :
No coupling means that a DM system will not utilize any function of a DB or DW system
.
 It may fetch data from a particular source ( such as a file system ), process data using some
data mining algorithms and then store the mining results in another file .
 Drawbacks : With out using a DB/DW system , A DM system may spend more amount of
time for finding , collecting ,cleaning and transforming data .
 No coupling represents a poor design .

2. Loose coupling :
Loose – coupling means that a DM system will use some facilities of a DB or DW system
, such as
 Fetching data from a data repository managed by these systems .

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
Performing data mining ,

And then storing the mining results either in a file or in a designed place in a
database or data warehouse .
 Loose coupling is better than no coupling because it can fetch any portion of data stored in
database or datawarehouses by using query processing , indexing and other system facilities.
 Drawbacks : It is difficult for loose coupling to achieve high scalabilityand good performance
with large data sets .

3. Semitight coupling
Semitight coupling means that besides linking a DM system to a DB/DW system ,
Efficient implementation of a few essential data mining primitives can be provided in the DB/DW
system .
 These primitives can include
 Sorting
 Indexing
 Aggregation
 Histogram analysis
 Multiway join
 Precomputation of some essential measures
 Sum
 Count
 Max
 Min
 Standard deviation
 Some frequently used intermediate mining results can be precomputed and stored in the
DB/DW system .

4. Tight coupling
Tight coupling means that a DM system is smoothly integrated into the DB/DW system .
 The data mining subsystem is treated as one functional component of an information system.
 Data mining queries and functions are optimized based on mining query analysis , data
structures, indexing schemes and query processing methods as DB or DW system .

1.8 Major issues in data mining

Major issues in data mining is regarding mining methodology, user interaction, performance, and
diverse data types

1. Mining methodology and user-interaction issues:

Mining different kinds of knowledge in databases: Since different users can be interested
in different kinds of knowledge, data mining should cover a wide spectrum of data analysis and
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knowledge discovery tasks, including data characterization, discrimination, association,
classification, clustering, trend and deviation analysis, and similarity analysis.
These tasks may use the same database in different ways and require the development of
numerous data mining techniques.
 Interactive mining of knowledge at multiple levels of abstraction:
Since it is difficult to know exactly what can be discovered within a database, the data
mining process should be interactive.
 Incorporation of background knowledge:
Background knowledge, or information regarding the domain under study, may be used
to guide the discovery patterns. Domain knowledge related to databases, such as integrity
constraints and deduction rules, can help focus and speed up a data mining process, or judge the
interestingness of discovered patterns.
 Data mining query languages and ad-hoc data mining:
Knowledge in Relational query languages (such as SQL) required since it allow users to
pose ad-hoc queries for data retrieval.
 Presentation and visualization of data mining results:
Discovered knowledge should be expressed in high-level languages, visual
representations, so that the knowledge can be easily understood and directly usable by humans
 Handling outlier or incomplete data:
The data stored in a database may reflect outliers: noise, exceptional cases, or incomplete
data objects. These objects may confuse the analysis process, causing over fitting of the data
to the knowledge model constructed. As a result, the accuracy of the discovered patterns can
be poor. Data cleaning methods and data analysis methods which can handle outliers are
required.
 Pattern evaluation: refers to interestingness of pattern:
A data mining system can uncover thousands of patterns. Many of the patterns discovered
may be uninteresting to the given user, representing common knowledge or lacking novelty.
Several challenges remain regarding the development of techniques to assess the
interestingness of discovered patterns .

2. Performance issues.
These include efficiency, scalability, and parallelization of data mining algorithms.
 Efficiency and scalability of data mining algorithms:
To effectively extract information from a huge amount of data in databases, data mining
algorithms must be efficient and scalable.
 Parallel, distributed, and incremental updating algorithms:
Such algorithms divide the data into partitions, which are processed in parallel. The
results from the partitions are then merged.

3. Issues relating to the diversity of database types

 Handling of relational and complex types of data:

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Since relational databases and data warehouses are widely used, the development of
efficient and effective data mining systems for such data is important.
 Mining information from heterogeneous databases and global information systems:
Local and wide-area computer networks (such as the Internet) connect many sources of
data, forming huge, distributed, and heterogeneous databases. The discovery of knowledge
from different sources of structured, semi-structured, or unstructured data with diverse data
semantics poses great challenges to data mining.

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