Conclusions and Future Research

This Chapter provides a critical review of the works presented in this Volume and also narrates the scope of potential applications of the different metaheuristic-based automatic clustering schemes in data mining, high level image processing and bioinformatics. The chapter ends with a discussion on the possible evolution of the proposed methods for handling clusters of non-spherical and shell type shapes, co-clustering and the problem of integrating together a feature selection module and a clustering module under the framework of Differential Evolution (DE).

[1]  Sushmita Mitra,et al.  Multi-objective evolutionary biclustering of gene expression data , 2006, Pattern Recognit..

[2]  John Quackenbush,et al.  Computational genetics: Computational analysis of microarray data , 2001, Nature Reviews Genetics.

[3]  Sham M. Kakade,et al.  On the sample complexity of reinforcement learning. , 2003 .

[4]  Oren Etzioni,et al.  Fast and Intuitive Clustering of Web Documents , 1997, KDD.

[5]  Doulaye Dembélé,et al.  Fuzzy C-means Method for Clustering Microarray Data , 2003, Bioinform..

[6]  Václav Snásel,et al.  Survey: Using Genetic Algorithm Approach in Intrusion Detection Systems Techniques , 2008, 2008 7th Computer Information Systems and Industrial Management Applications.

[7]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[8]  L. Bregman The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming , 1967 .

[9]  Sanghamitra Bandyopadhyay,et al.  A Point Symmetry-Based Clustering Technique for Automatic Evolution of Clusters , 2008, IEEE Transactions on Knowledge and Data Engineering.

[10]  Witold Pedrycz,et al.  Special Issue on Bioinformatics , 2006, Pattern Recognit..

[11]  Huan Liu,et al.  Subspace clustering for high dimensional data: a review , 2004, SKDD.

[12]  F. Attneave Symmetry, information, and memory for patterns. , 1955, The American journal of psychology.

[13]  Huan Liu,et al.  Discretization: An Enabling Technique , 2002, Data Mining and Knowledge Discovery.

[14]  Gareth Jones,et al.  Non-hierarchic document clustering using a genetic algorithm , 1995, Information Research.

[15]  Jonathan M. Garibaldi,et al.  The Application of a Simulated Annealing Fuzzy Clustering Algorithm for Cancer Diagnosis , 2004 .

[16]  Inderjit S. Dhillon,et al.  Clustering with Bregman Divergences , 2005, J. Mach. Learn. Res..

[17]  Terrence P. Fries,et al.  A fuzzy-genetic approach to network intrusion detection , 2008, GECCO '08.

[18]  Federico Divina,et al.  Biclustering of expression data with evolutionary computation , 2006, IEEE Transactions on Knowledge and Data Engineering.

[19]  Thomas E. Potok,et al.  Document clustering using particle swarm optimization , 2005, Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..

[20]  George Karypis,et al.  Empirical and Theoretical Comparisons of Selected Criterion Functions for Document Clustering , 2004, Machine Learning.

[21]  Feng Cao,et al.  Impact of discretization methods on the rough set-based classification of remotely sensed images , 2011, Int. J. Digit. Earth.

[22]  Joshua D. Knowles,et al.  An Evolutionary Approach to Multiobjective Clustering , 2007, IEEE Transactions on Evolutionary Computation.

[23]  James Kennedy,et al.  The Behavior of Particles , 1998, Evolutionary Programming.

[24]  Hans-Hermann Bock,et al.  Two-mode clustering methods: astructuredoverview , 2004, Statistical methods in medical research.

[25]  Nikhil R. Pal,et al.  Genetic programming for simultaneous feature selection and classifier design , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[26]  Mehmet Fatih Tasgetiren,et al.  A discrete differential evolution algorithm for the permutation flowshop scheduling problem , 2007, GECCO '07.

[27]  T. K. Baker,et al.  Temporal gene expression analysis of monolayer cultured rat hepatocytes. , 2001, Chemical research in toxicology.

[28]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[29]  Daphne Koller,et al.  Hierarchically Classifying Documents Using Very Few Words , 1997, ICML.

[30]  Dorothy E. Denning,et al.  An Intrusion-Detection Model , 1987, IEEE Transactions on Software Engineering.

[31]  Sean Luke,et al.  Cooperative Multi-Agent Learning: The State of the Art , 2005, Autonomous Agents and Multi-Agent Systems.

[32]  Chien-Hsing Chou,et al.  Short Papers , 2001 .

[33]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[34]  Todd L. Heberlein,et al.  Network intrusion detection , 1994, IEEE Network.

[35]  James C. Bezdek,et al.  On cluster validity for the fuzzy c-means model , 1995, IEEE Trans. Fuzzy Syst..

[36]  Andries Petrus Engelbrecht,et al.  A Color Image Quantization Algorithm Based on Particle Swarm Optimization , 2005, Informatica.

[37]  Eugene H. Spafford,et al.  Applying Genetic Programming to Intrusion Detection , 1995 .

[38]  Gerald Kowalski,et al.  Information Retrieval Systems: Theory and Implementation , 1997 .

[39]  U. Alon,et al.  Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[40]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[41]  Arlindo L. Oliveira,et al.  Biclustering algorithms for biological data analysis: a survey , 2004, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[42]  Bernd Freisleben,et al.  An evolutionary approach to color image quantization , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[43]  James Kennedy,et al.  The particle swarm: social adaptation of knowledge , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[44]  George Karypis,et al.  A Comparison of Document Clustering Techniques , 2000 .

[45]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[46]  Zdzislaw Pawlak,et al.  Rough Set Theory and its Applications to Data Analysis , 1998, Cybern. Syst..

[47]  Ujjwal Maulik,et al.  Multiobjective Genetic Clustering for Pixel Classification in Remote Sensing Imagery , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[48]  Z. Pawlak Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[49]  Hussein A. Abbass,et al.  Biologically-inspired Complex Adaptive Systems approaches to Network Intrusion Detection , 2007, Inf. Secur. Tech. Rep..

[50]  Vijay V. Raghavan,et al.  A clustering strategy based on a formalism of the reproductive process in natural systems , 1979, SIGIR 1979.

[51]  Peter Dayan,et al.  Technical Note: Q-Learning , 2004, Machine Learning.

[52]  Axel van Lamsweerde,et al.  Learning machine learning , 1991 .

[53]  Z. Pawlak Reasoning about Knowledge , 1991 .

[54]  Michael L. Littman,et al.  Efficient Reinforcement Learning with Relocatable Action Models , 2007, AAAI.

[55]  Ali M. S. Zalzala,et al.  Towards effective subspace clustering with an evolutionary algorithm , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..