Data mining involves extracting hidden patterns from large amounts of data. It has various applications in library and information science for analyzing user data to determine customer preferences, predict user behavior, and identify frequently used resources. The document outlines the data mining process, which includes data selection, cleaning, transformation, mining, and interpretation. Data mining techniques can be used to analyze citation patterns, formulate statistical models of library services, and facilitate knowledge organization on the web.

1. DATA MINING AND ITS APPLICATION
IN LIBRARYAND INFORMATION
SCIENCE
Dr. Santosh Kumar Tunga
[ORCID: 0000-0001-5534-4861]
Librarian, R B C Evening College, Naihati
North 24 Parganas, West Bengal, India
tungask@rediffmail.com
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2.  Database Management Systems gave access to the
data stored but this was only a small part of what
could be gained from the data. Traditional on line
transaction processing system are good at putting
data into databases quickly, safely and efficiently
but are not good at delivering meaningful analysis
in return. Extraction of interesting information or
patterns from data in large database is known as
Data Mining.
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3.  Data Mining, also called as data archeology, data
dredging data harvesting, is the process of
extracting hidden knowledge from large volumes
of raw data and using it to make crucial library
decisions. This is where Data Mining or
knowledge Discovery in Databases has obvious
benefit for any enterprise
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4.  DM or Knowledge Discover in Database is
concerned with the analysis of data and the use of
software techniques for finding patterns and
regularities in sets of data.
 Extraction of interesting information or patterns
from data in large database is known as DM.
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5.  DM is the process of analyzing large amount of
data in search of previously undiscovered
information pattern.
 DM refers to using a variety of techniques to
identify nuggets of information or decision making
knowledge in bodies of data and extracting these in
such a way that they can be put to use in the areas
such as decision support prediction, forecasting
and estimation
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6.  Many Digital Libraries utilize Data Mining technology in
order to facilitate advanced future prospective services and
also DL users research.
 A couple of Digital Libraries use data mining technology to
analysis GIS data and a data services unit facilities user’s
analysis of social sciences computing data.
 Data Mining tools are use to perform text analysis and
clustering in large Open Archives Initiative Meta data
repositories.
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7.  Many Digital Libraries (DLs) and Information centers
use data mining technology to facilitate their library's
own administrative or strategic purposes.
 To modernize own existing DLs Data Mining play an
important role.
 Data Mining is a step within the KDD process through
which an organization's data assets are processed and
analyzed to gain insights to assist decision making.
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8. Fig-1 Multi- disciplinary approach in Data Mining
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9. 5. Interpretation Knowledge
4. Data Mining / Pattern Recognition
3. Transformation
2. Data Enrichment
1. Data selection and
cleaning
Fig 2. An Overview of the steps that compose the Data Mining Process
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10.  Data Selection
There are two parts to selecting data for data mining.
The first part, locating data, tends to be more
mechanical in nature than the second part, identifying
data, which requires significant input by a domain
expert for the data.
 Data Cleaning
Data cleaning is the process of ensuring that for data
mining purposes, the data is uniform in terms of key
and attribute usage. Data cleaning attempts to correct
misused or incorrect attributes in existing data.
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11.  Data Enrichment (DE)
DE is the process of adding new attributes such as
calculated fields or data from external sources to existing
data. It involves adding information to existing data. This
can include combining internal data with external data,
obtained from either different departmental libraries or
information centers.
 Data Transformation (DT)
DT is the process of changing the form or structure of
existing attributes. It is separate from data cleaning and
data enrichment for data mining purposes because it does
not correct existing attribute data or add new attributes but
instead grooms existing attributes for data mining
purposes.
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12.  Data Mining (DM)
DM is not so much a single technique as the idea that there
is more knowledge hidden in the data than shows itself on
the surface. From this point of view DM is really an
anything goes affair.
 Visualization/ Interpretation/ Evaluation
Visualization techniques are a very useful method
discovering patterns in datasets. Advanced graphical
techniques virtual reality enable people to wander through
artificial spaces, white historic development of data sets can
be displayed as a kind of animated movies.
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13. Fig-3 Architecture of Data Mining
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14.  Database, Data Warehouse or other Information
Repository:
This is one or a set of databases, data warehouses,
spreadsheets or other kinds of information repositories.
Data cleaning and data integration techniques may be
performed on the data.
 Database or Data Warehouse Server:
The database or data warehouse server is responsible for
fetching the relevant data, based on the user's data mining
request.
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15.  Knowledge Base
This is domain knowledge that is used to guide the search
or evaluate the interestingness of resulting patterns. Such
knowledge can include concept hierarchies, used to
organize attributes values into different levels of
abstraction.
 Data Mining Engine
This is essential to the data mining system and ideally
consists of a set of functional modules for tasks such as
characterization, association, classification, cluster
analysis and evaluation and deviation analysis.
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16.  Pattern evaluation module
This component typically employs interestingness
measures and interacts with the data mining
modules so as to focus the search towards
interesting patterns.
 Graphical user Interface
This module allows the user to browse database
and data warehouse schemes or structures;
evaluate mined patterns and visualize the patterns
in different forms.
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17.  Identify library customers who will be most receptive to
new service offers.
 Identify the most effective library customers.
 Find out the library customers who will opt for each type
of service in the following year.
 Predict library customer reaction to the change of library
services.
 Determine customer preference of the different modes of
library transaction.
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18.  Analyzing the most frequently prescribed useful
documents.
 Demand analysis and forecasting helps the library
authority to determine the optimum levels of
library stocks.
 Management of library resources and network
traffic required different.
 Formulating statistical modeling of library
services.
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19.  Application of statistical techniques in citation
studies, bibliometric coupling, correlation and other
areas of informetrics and scientometrics helps in data
mining.
 Methods for knowledge organization in the web are
based on theories, principles and practices that
librarians have long formulated, understood and
applied e.g. classification schemes, subject headings,
authority files and thesauri etc.
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20.  Web mining is mining of data related to the world wide
web or data related to web pages / web activity
 Web mining is the application of DM techniques to
discover patterns from the web
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21. Web data can be classifies into the following classes:-
 Content of actual web pages
 Intra-page structure is actual linkage structure between web.
 Inter-page structure is actual linkage structure between web.
 Usage data that describe how web pages are accessed by
visitors.
 User profiles include demographic and registration
information obtained about users.
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22. The following are the classification of Web Mining:-
 Web content mining
 Web page content mining
 Search result mining
 Web structure mining
 Web usage mining
 General access pattern tracking
 Customized usage tracking
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23. Data Mining is an effective set of analysis tools and
techniques used in the decision support process.
 Data Mining is not a ‘black box’ process in which the
data miner simply builds a data mining models and
watches as meaningful information appears.
 Data Mining does not guarantee the behavior of future
data through the analysis of historical data.
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24.  Data Mining is a guidance tool, used to provide
insight into the trends inherent in historical
information.
 Data Mining is the process of analyzing data from
different perspective and summarizing it into useful
information so that it can be used to increase revenue,
cuts costs or both.
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25.  Appletion, Elaine. (1995). The Right Server
for your Data Warehouse. Datamation,
41(5), 75-84.
 Brachman, R.J. [etc.al]. (1996). Mining
Business Database Communications of the
ACM. 39(11), 32.
 Dunhan, Margaret H. (2004). Data Mining
Introductory and Advanced Topics. New
Delhi: Pearson Education.
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26.  Han, J. & Kamber, M. (2000). Data Mining :
Concepts and Techniques. New Delhi:
Morgan Kaufman. 115.
 Jean-Mare, Adamo. (2000). Data Mining for
Association Rules and Sequential Patterns,
New York: Springer-Verlag. 56.
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