This document outlines a class on data mining issues. It discusses five groups of crucial implementation issues in data mining: mining methodology, user integration, efficiency and scalability, diversity of data types, and data mining and society. Specific issues covered include developing new mining techniques, interactive mining, handling noisy or incomplete data, and presenting results visually. The goal is for students to understand the key issues that arise in data mining.

1. CT Department III BSC CT Even Semester 2019 - 20
Unit I DATA MINING ISSUES 1DATA MINING
Course: Data Mining Sub Code: 6ED
Google Classroom: q7b4gv Programme: B.Sc-CT
Unit: I Hour : 6
DATA MINING ISSUES
FACULTY : Ms.A.SATHIYA PRIYA

2. CT Department III BSC CT Even Semester 2019 - 20
Unit I DATA MINING ISSUES 1DATA MINING
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Department of Computer Technology III BSC CT SEM V Year:
2019- 20
UNIT I Basic Data Mining Tasks6ED – Data Mining
SNAP TALK
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3. CT Department III BSC CT Even Semester 2019 - 20
Unit I DATA MINING ISSUES 1DATA MINING
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Department of Computer Technology III BSC CT SEM V Year:
2019- 20
UNIT I Basic Data Mining Tasks6ED – Data Mining
ATTENDANCE
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4. CT Department III BSC CT Even Semester 2019 - 20
Unit I DATA MINING ISSUES 1DATA MINING
Expected outcome
 The outcome of this session is to
understand about the Data Mining Issues.

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Unit I DATA MINING ISSUES 1DATA MINING
 There are some crucial implementation issues associated
with data mining.
 Partitioning them into five groups,
 Mining methodology
 User integration
 Efficiency and Scalability
 Diversity of data types
 Data mining and Society
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DATA MINING ISSUES

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Mining Methodology
 Mining various and new kinds of knowledge: Data
mining covers a wide a spectrum of data analysis and
knowledge discovery tasks.
 The data mining tasks may use same database in
different ways.
 It require the development of numerous data mining
techniques.
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Cont..,
 Due to the diversity of applications, new mining tasks
continue to emerge, making data mining a dynamic
and fast growing field.
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Mining knowledge in multidimensional space
 When searching for knowledge in large data sets.
 It can explore the data in multidimensional space.
 Search for interesting patterns among the combinations
of dimensions at varying levels of abstraction.
 Data can be aggregated or viewed as a multidimensional
data cube.
 Mining knowledge in cube space can substantially
enhance the power and flexibility of data mining.
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Department of CT III-B.Sc-CS VI Semester: 2017-18
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Data mining-an interdisciplinary effort
 The power of data mining can be substantially
enhanced by integrating new methods from multiple
disciplines.
 The mining of software bugs in large programs.
 This form of mining known as bug-mining.
 The incorporation of software engineering knowledge
into the data mining process.
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Boosting the power of discovery in a networked
environment
 Most data objects reside in a linked or interconnected
environment.
 It be the web, databases relations, files or
documents.
 Semantic links across multiple data objects can be
used to advantage in data mining.
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Department of CT III-B.Sc-CS VI Semester: 2017-18
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Cont..,
 Knowledge derived in one set of objects can be used
to boost the discovery of knowledge in a “related” or
semantically linked set of objects.
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Handling uncertainty, noise of incompleteness of
data
 Data often contain noise, errors, exceptions or
uncertainty, or are incomplete.
 Errors and noise may confuse the data mining
process, leading to the derivation of erroneous
patterns.
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Department of CT III-B.Sc-CS VI Semester: 2017-18
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Cont..,
 Data cleaning, data preprocessing, outlier detection
and removal, and uncertainty reasoning.
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Missing Data
 There may be missing variable values, incomplete
data.
 Some algorithms require complete data.
 Missing values have to be estimated or variables with
very frequent missing values perhaps to be removed.
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Irrelevant Data
 Some variables may be useless.
 If all values of a variable are constant, it is called
dead and can be removed.
 If almost all values are constant, it is not
straightforward whether it can be removed.
 Those very rare values could be essential in some
situations.
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Noisy Data
 Some values might be invalid or incorrect.
 A user or a measuring equipment has given a
false value.
 These are corrected or deleted, but first they have
to be found.
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Outliers
 There are sometimes many data entries that do not
fit nicely into the derived model.
 They may be erroneous values or otherwise
exceptional that are best to remove.
 For instance, the age of 0 year for patient data is
such.
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Pattern evaluation and pattern or constraint guided
mining
 A pattern interesting may vary from user to user.
 Techniques are needed to asses the interestingness of
discovered patterns based on subjective measures.
 The value patterns with respect to a given user class,
based on user beliefs or expectations.
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User Interaction
 Interactive Mining: The data mining process should be
highly interactive.
 It is important to build flexible user interfaces.
 An exploratory mining environment, facilitating the users
interaction with the system.
 First sample a set of data, explore general characteristics
of the data and estimate potential mining results.
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Cont..,
 Data mining problems are often not precisely stated,
both application domain and data mining experts are
needed.
 Training data and results desired are defined.
 Interpretation of results is important to do carefully.
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Incorporation of background knowledge
 Background knowledge, constraints, rules and other
information regarding the domain under study should
be incorporated into the knowledge discovery
process.
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Presentation and visualization of data mining results
 A data mining system present data mining results,
vividly and flexibility.
 The system to adopt expressive knowledge
representations, user friendly interfaces and
visualization techniques.
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Interpretation
 This may require experts to correctly interpret the
results obtained.
Visualization
 To easily view and understand input data and results
visualization is helpful.
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Large Datasets
 Data set may be massive which create problems to
handle such.
 Sampling and parallelization are effective to attack
these problems.
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Multimedia Data
 Usually data mining methods are targeted to
traditional data types, i.e., numeric, characters an
text.
 They are not always suitable for multimedia, e.g.,
geographic data (GIS).
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Changing Data
 Data cannot be assumed to be static even if mostly
we start from this thought.
 Therefore, algorithms must be rerun from time to
time.
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Over fitting
 Over fitting occurs when a model is built to be too
detailed or strictly fit the data given.
 Thus, it may lose its generalization ability and is not
valid for future data.
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Points to ponder
 The data mining tasks may use same database in
different ways.
 It require the development of numerous data mining
techniques.
 The data mining process should be highly interactive.
 A data mining system present data mining results,
vividly and flexibility.

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Keywords
same database in different ways.
input data and results visualization
rerun from time to time.
not valid for future data.
Sampling and parallelization.

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MCQ’S
1. Many data entries that do not fit nicely into the ______model
A. Concurrent b. Derived c. Algorithm
2. To easily view and understand input data and results
________is helpful.
A. Visualization b. Related information C. Comparision
3. A data mining system present data mining results, vividly
and________.
A. Easy b. Compatibility c. Flexibility

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Answers
1. b. derived
2. a. visualization
3. c. flexibility

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THANK U
Department of Computer Technology III BSC CT SEM V year: 2019-
20
6ED – Data Mining UNIT I Basic Data Mining Tasks