Wise mining method through ant colony optimization

Yang Jianxiong; Junzo Watada

doi:10.1109/ICSMC.2009.5346807

Wise mining method through ant colony optimization

Yang Jianxiong, Junzo Watada

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper proposes an algorithm for data mining named Pheromone-Miner (ant-colony-based data miner). The algorithm is inspired by both researches on the behavior of real ant colonies and data mining concepts as well as principles. The goal of Pheromone-Miner is to extract more exact knowledge from a database. Pheromone-based mining breaks through limitations of other mining approaches. We compare the performance of pheromone-miner with a general semantic miner. The accident causes discovered by ant-miner are considerably more accurate than those discovered by a general semantic miner. In a word, this evolutionary algorithm is suitable for improving the accuracy of data miners.

Original language	English
Title of host publication	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Pages	1833-1839
Number of pages	7
DOIs	https://doi.org/10.1109/ICSMC.2009.5346807
Publication status	Published - 2009
Event	2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009 - San Antonio, TX Duration: 2009 Oct 11 → 2009 Oct 14

Other

Other	2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009
City	San Antonio, TX
Period	09/10/11 → 09/10/14

Keywords

Ant colony optimization algorithm
Data mining
Knowledge discovery
Pheromone

ASJC Scopus subject areas

Electrical and Electronic Engineering
Control and Systems Engineering
Human-Computer Interaction

Access to Document

10.1109/ICSMC.2009.5346807

Fingerprint Dive into the research topics of 'Wise mining method through ant colony optimization'. Together they form a unique fingerprint.

Cite this

@inproceedings{f5762c22f56a4e4fa65ce3e7af97978f,

title = "Wise mining method through ant colony optimization",

abstract = "This paper proposes an algorithm for data mining named Pheromone-Miner (ant-colony-based data miner). The algorithm is inspired by both researches on the behavior of real ant colonies and data mining concepts as well as principles. The goal of Pheromone-Miner is to extract more exact knowledge from a database. Pheromone-based mining breaks through limitations of other mining approaches. We compare the performance of pheromone-miner with a general semantic miner. The accident causes discovered by ant-miner are considerably more accurate than those discovered by a general semantic miner. In a word, this evolutionary algorithm is suitable for improving the accuracy of data miners.",

keywords = "Ant colony optimization algorithm, Data mining, Knowledge discovery, Pheromone",

author = "Yang Jianxiong and Junzo Watada",

year = "2009",

doi = "10.1109/ICSMC.2009.5346807",

language = "English",

isbn = "9781424427949",

pages = "1833--1839",

booktitle = "Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics",

note = "2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009 ; Conference date: 11-10-2009 Through 14-10-2009",

}

TY - GEN

T1 - Wise mining method through ant colony optimization

AU - Jianxiong, Yang

AU - Watada, Junzo

PY - 2009

Y1 - 2009

N2 - This paper proposes an algorithm for data mining named Pheromone-Miner (ant-colony-based data miner). The algorithm is inspired by both researches on the behavior of real ant colonies and data mining concepts as well as principles. The goal of Pheromone-Miner is to extract more exact knowledge from a database. Pheromone-based mining breaks through limitations of other mining approaches. We compare the performance of pheromone-miner with a general semantic miner. The accident causes discovered by ant-miner are considerably more accurate than those discovered by a general semantic miner. In a word, this evolutionary algorithm is suitable for improving the accuracy of data miners.

AB - This paper proposes an algorithm for data mining named Pheromone-Miner (ant-colony-based data miner). The algorithm is inspired by both researches on the behavior of real ant colonies and data mining concepts as well as principles. The goal of Pheromone-Miner is to extract more exact knowledge from a database. Pheromone-based mining breaks through limitations of other mining approaches. We compare the performance of pheromone-miner with a general semantic miner. The accident causes discovered by ant-miner are considerably more accurate than those discovered by a general semantic miner. In a word, this evolutionary algorithm is suitable for improving the accuracy of data miners.

KW - Ant colony optimization algorithm

KW - Data mining

KW - Knowledge discovery

KW - Pheromone

UR - http://www.scopus.com/inward/record.url?scp=74849117644&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=74849117644&partnerID=8YFLogxK

U2 - 10.1109/ICSMC.2009.5346807

DO - 10.1109/ICSMC.2009.5346807

M3 - Conference contribution

AN - SCOPUS:74849117644

SN - 9781424427949

SP - 1833

EP - 1839

BT - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

T2 - 2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009

Y2 - 11 October 2009 through 14 October 2009

ER -