Parallel Ant Miner 2

In this paper, we propose a flexible parallel ant colony algorithm for classification rule discovery in the large databases. We call this algorithm Parallel Ant-Miner2. This model relies on the extension of real behavior of ants and data mining concepts. The artificial ants are firstly generated and separated into several groups. Each group is assigned a class label which is the consequent parts of the rules it should discover. Ants try to discover rules in parallel and then communicate with each other to update the pheromones in different paths. The communication methods help ants not to gather irrelevant terms of the rule. The parallel executions of ants reduce the speed of convergence and consequently make it possible to extract more new high quality rules by exploring all search space. Our experimental results show that the proposed model is more accurate than the other versions of Ant-Miner.

[1]  Alex Alves Freitas,et al.  A new version of the ant-miner algorithm discovering unordered rule sets , 2006, GECCO '06.

[2]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[3]  Chuei-Tin Chang,et al.  A fuzzy diagnosis approach using dynamic fault trees , 2002 .

[4]  Ian Witten,et al.  Data Mining , 2000 .

[5]  Alex Alves Freitas,et al.  Data mining with an ant colony optimization algorithm , 2002, IEEE Trans. Evol. Comput..

[6]  Ramaswamy Vaidyanathan,et al.  Process fault detection and diagnosis using neural networks , 1990 .

[7]  Bo Liu,et al.  Density-Based Heuristic for Rule Discovery with Ant-Miner , 2002 .

[8]  Hussein A. Abbass,et al.  Classification rule discovery with ant colony optimization , 2003, IEEE/WIC International Conference on Intelligent Agent Technology, 2003. IAT 2003..

[9]  Shusaku Tsumoto,et al.  Foundations of Intelligent Systems, 15th International Symposium, ISMIS 2005, Saratoga Springs, NY, USA, May 25-28, 2005, Proceedings , 2005, ISMIS.

[10]  Julian F. Miller,et al.  Genetic and Evolutionary Computation — GECCO 2003 , 2003, Lecture Notes in Computer Science.

[11]  Ling Chen,et al.  Parallel ant colony algorithm for mining classification rules , 2006, 2006 IEEE International Conference on Granular Computing.

[12]  Alex Alves Freitas,et al.  Discovering interesting knowledge from a science and technology database with a genetic algorithm , 2004, Appl. Soft Comput..

[13]  Peter Clark,et al.  Rule Induction with CN2: Some Recent Improvements , 1991, EWSL.

[14]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[15]  D. Himmelblau,et al.  Detection of leaks in a liquid-liquid heat exchanger using passive acoustic noise , 1996 .

[16]  Jeng-Shyang Pan,et al.  Parallel Ant Colony Systems , 2003, ISMIS.