A new efficient approach for data clustering in electronic library using ant colony clustering algorithm
| Published date | 01 July 2006 |
| DOI | https://doi.org/10.1108/02640470610689223 |
| Pages | 548-559 |
| Date | 01 July 2006 |
| Author | An‐Pin Chen,Chia‐Chen Chen |
| Subject Matter | Information & knowledge management,Library & information science |
A new efficient approach for data
clustering in electronic library
using ant colony clustering
algorithm
An-Pin Chen and Chia-Chen Chen
Institute of Information Management, National Chiao-Tung University,
Hsinchu, Taiwan
Abstract
Purpose – Traditional library catalogs have become inefficient and inconvenient in assisting library
users. Readers may spend much time in searching library materials via printed catalogs. Readers need
an intelligent and innovative solution to overcome this problem. The purpose of this paper is to
illustrate how data mining technology is a good approach to fulfill readers’ requirements.
Design/methodology/approach – Data mining is considered to be the nontrivial extraction of
implicit, previously unknown, and potentially useful information from data. This paper analyzes the
readers’ borrowing records by using the following techniques: data analysis, building data warehouse
and data mining.
Findings – The mining results show that all readers can be categorized into five clusters, and each
cluster has its own characteristics. It was also found that the frequency for graduates and associate
researchers to borrow multimedia data is much higher. This phenomenon shows that these readers
have a higher preference for accepting digitized publication. Besides, we notice that more readers
borrow multimedia data rise in years. This up trend indicates that readers are gradually shifting their
preference in reading digital publications.
Originality/value – The paper proposes a technique to discover clusters by using ant colony
methods.
Keywords Digital libraries,Cluster analysis, Data collection,Data analysis
Paper type Case study
1. Introduction
Database systems that collect, analyze, and transfer data are used for various
mid-range and large organizations. Over time, more and more current, detailed, and
accurate data are accumulated and stored in databases with various stages. These data
may be related to designs, products, machines, materials, processes, inventories, sales,
marketing, and performance data. They may include patterns, trends, associations,
and dependencies. The collected data contain valuable information that could be
integrated into the organization’s strategy, and be used to improve organizational
decisions. Consequently, data mining methods become important tools in today’s
society.
Data mining is the process of extracting valid, previously unknown,
comprehensible information from large databases in order to improve and optimize
organization decisions (Anand and Buchner, 1998). The term knowledge discovery in
database (KDD) is used to denote the entire process of turning low-level data into
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0264-0473.htm
EL
24,4
548
Received 12 April 2005
Revised 12 November 2005
Accepted December 2005
The Electronic Library
Vol. 24 No. 4, 2006
pp. 548-559
qEmerald Group Publishing Limited
0264-0473
DOI 10.1108/02640470610689223
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