Agent-Based Data Compression Supporting Knowledge Discovery in Mobile Environment

Authors:
Romeo Mark Mateo;Hwang Jae-Jeong;Jaewan Lee
Affiliations:
School of Electronic and Information Engineering, Kunsan National University, 68 Miryong-dong, Kunsan, Chonbuk 573-701, South Korea;School of Electronic and Information Engineering, Kunsan National University, 68 Miryong-dong, Kunsan, Chonbuk 573-701, South Korea;School of Electronic and Information Engineering, Kunsan National University, 68 Miryong-dong, Kunsan, Chonbuk 573-701, South Korea
Venue:
KES-AMSTA '07 Proceedings of the 1st KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
Year:
2007

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Abstract

Location-aware services using data mining techniques are recent research topics where rules from the data are extracted to provide interesting information. In addition, multi-agent systems are applied in location-based service for autonomous interaction of the system. Different data mining techniques are applied for knowledge discovery from location-based services. However, wireless environment limits the transmission of large data and possible for errors. This work presents a multi-agent framework for the location-based service using data mining. To support the data mining, a data compressor agent (DCA) based on neuro-fuzzy classifier is proposed. DCA performs data preprocessing where it merges the less frequent dataset by using neuro-fuzzy classifier before sending the data. User agent processes the knowledge discovery by using data mining like association rule mining. The result shows the proposed neuro-fuzzy data compression is more efficient compressor.