Yusof, Yuhanis and Baharom, Fauziah and Mohamed, Athraa Jasim (2018) Optimizing the Management of Knowledge Assets using Swarm Intelligence. In: Knowledge Management International Conference (KMICe) 2018, 25 –27 July 2018, Miri Sarawak, Malaysia.

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Abstract

Knowledge assets are the knowledge drivers of an organization’s success and they can be of structured, unstructured, tacit or explicit knowledge. Explicit knowledge are realized as documents and these documents need to be tagged in order to ease the process of retrieval.Such document grouping process can help an organisation to meet legal and regulatory requirements for retrieving specific information in a set timeframe. Current document clustering techniques rely on a pre-defined value of k (number of clusters). Hence, the produced clusters will be of different quality. This study presents the employment of swarm intelligence algorithm, i.e Firefly Algorithm, to automatically cluster text document without the use of k value. Experimental results shows that the performance of the algorithm is better compared to the benchmark methods. The number of obtained clusters are the same as the ones defined in the data collection while the purity value for three out of four datasets are higher than the benchmark methods. Hence, this indicates that the proposed swarm intelligence based clustering facilitates the grouping of knowledge assets. By having an automated document clustering, tagging the document with their appropriate label will help organization to better manage their knowledge assets.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	ISBN: 9789670910871 Organized by: School of Computing, College of Arts and Sciences, Universiti Utara Malaysia.
Uncontrolled Keywords:	Knowledge assets, document clustering, swarm intelligence, firefly algorithm, data mining.
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Divisions:	School of Computing
Depositing User:	Mrs. Norazmilah Yaakub
Date Deposited:	28 Nov 2018 00:40
Last Modified:	28 Nov 2018 00:40
URI:	https://repo.uum.edu.my/id/eprint/25247

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