Differential Evolution Memetic Document Clustering Using Chaotic Logistic Local Search

In this paper, we propose a Memetic-based clustering method that improves the partitioning of document clustering. Our proposed method is named as Differential Evolution Memetic Clustering (DEMC). Differential Evolution (DE) is used for the selection of the best set of cluster centres (centroids) while the Chaotic Logistic Search (CLS) is used to enhance the best set of solutions found by DE. For the purpose of comparison, the DEMC is compared with the basic DE, Differential Evolution Simulated Annealing (DESA) and the Differential Evolution K-Means (DEKM) methods as well as the traditional partitioning clustering using the K-means. The DEMC is also compared with the recently proposed Chaotic Gradient Artificial Bee Colony (CGABC) document clustering method. The reuters-21578, a pair of the 20-news group, classic 3 and TDT benchmark collection (TDT5) along with real-world six-event-crimes datasets are used in the experiments in this paper. The results showed that the proposed DEMC outperformed the other methods in terms of the convergence rate measured by the fitness function (ADDC) and the compactness of the resulted clusters measured by the F-macro and F-micro measures.

[1]  Oliver Kramer,et al.  Derivative-Free Optimization , 2011, Computational Optimization, Methods and Algorithms.

[2]  Xavier Blasco Ferragud,et al.  Hybrid DE algorithm with adaptive crossover operator for solving real-world numerical optimization problems , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[3]  Amit Konar,et al.  Document Clustering Using Differential Evolution , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[4]  Kevin Kok Wai Wong,et al.  Classification of adaptive memetic algorithms: a comparative study , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  Chunmei Zhang,et al.  Distributed memetic differential evolution with the synergy of Lamarckian and Baldwinian learning , 2013, Appl. Soft Comput..

[6]  Chunming FU,et al.  Improved Differential Evolution with Shrinking Space Technique for Constrained Optimization , 2017 .

[7]  Ganapati Panda,et al.  A survey on nature inspired metaheuristic algorithms for partitional clustering , 2014, Swarm Evol. Comput..

[8]  Hao Zheng,et al.  A novel clustering-based differential evolution with 2 multi-parent crossovers for global optimization , 2012, Appl. Soft Comput..

[9]  Wei Song,et al.  A hybrid evolutionary computation approach with its application for optimizing text document clustering , 2015, Expert Syst. Appl..

[10]  Li-Li Bing.  Chaos Optimization Method and Its Application , 1997 .

[11]  Ilpo Poikolainen,et al.  Differential Evolution with Concurrent Fitness Based Local Search , 2013, 2013 IEEE Congress on Evolutionary Computation.

[12]  Elizabeth León Guzman,et al.  Web document clustering based on a new niching Memetic Algorithm, Term-Document Matrix and Bayesian Information Criterion , 2010, IEEE Congress on Evolutionary Computation.

[13]  Pramod Kumar Singh,et al.  Chaotic gradient artificial bee colony for text clustering , 2016, Soft Comput..

[14]  Jian Peng,et al.  2016 Ieee International Conference on Big Data (big Data) Exploiting Temporal Divergence of Topic Distributions for Event Detection , 2022 .

[15]  Wojciech Kwedlo,et al.  A clustering method combining differential evolution with the K-means algorithm , 2011, Pattern Recognit. Lett..

[16]  Lei Peng,et al.  Memetic Differential Evolution with an Improved Contraction Criterion , 2017, Comput. Intell. Neurosci..

[17]  Milos Manic,et al.  Clustering of web search results based on an Iterative Fuzzy C-means Algorithm and Bayesian Information Criterion , 2013, 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS).

[18]  Muhammad Khurram Khan,et al.  An effective memetic differential evolution algorithm based on chaotic local search , 2011, Inf. Sci..

[19]  M. Emre Celebi,et al.  Partitional Clustering Algorithms , 2014 .

[20]  Mohammad Reza Meybodi,et al.  Efficient stochastic algorithms for document clustering , 2013, Inf. Sci..

[21]  H. Saruhan.  Differential evolution and simulated annealing algorithms for mechanical systems design , 2014 .

[22]  Zhijian Wu,et al.  An Enhanced Differential Evolution with Elite Chaotic Local Search , 2015, Comput. Intell. Neurosci..