|Article title||COMBINED BIOINSPIRED ALGORITHM FOR SOLVING THE CLUSTERING PROBLEM|
|Authors||A. N. Natskevich, I. O. Kursitys|
|Section||SECTION IV. DATA ANALYSIS AND KNOWLEDGE MANAGEMENT|
|Month, Year||04, 2018 @en|
|Abstract||The article is devoted to solving the clustering problem, which is one of the most important and popular problem in intelligent data analysis. Clustering, which means uniting the similar elements in groups, is one of the fundamental problem in Data Mining. Application of solving this problem includes image segmentation, marketing, protection from financial fraud, forecasting, text analysis and many other fields. A constantly growing scope of generated, transferred and processed data determines the significance of the problem. The authors investigate the clustering problem, provide the problem statement, the main mathematical formulas and the objective function needed for solving. The article consists of the analytical review of the popular algorithms, such as hierarchical optimization, squared error, k-means, c-means and graph-based algorithms. The authors note their benefits and shortcomings. The clustering problem is NP-complete, which determines the advantages of application of bioinspired models and methods for solving the mentioned problem. The related works of famous researchers are given in the article, such as: particle swarm optimization, ant colony optimization, artificial bee colony algorithm, bacteria colony optimization, cuckoo search algorithm, which demonstrate the effectiveness of bioinspired algorithms in terms of solving the clustering problem. The authors propose a combined bioinspired algorithm which applies the ant colony optimization and the bat algorithm successively. The main ideas of the algorithms, their flowcharts and solutions encoding schemes are provided herein. The local search method is implemented in the bat algorithm. The experiments carried out with benchmarks demonstrate the effectiveness of the proposed algorithm in comparison with the k-means algorithm and the genetic one. During the experimental research the authors managed to define the developed combined bioinspired algorithm time complexity. The authors are planning to apply the suggested combined solution for boosting of the algorithms, which works with several algorithms and reveals the best solution among several ones obtained with different bioinspired algorithms.|
|Keywords||Clustering; bioinspired algorithm; ant colony optimization; bat algorithm; swarm intelligence, artificial intelligence.|
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