Adaptive Ant Clustering Algorithm with Pheromone
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- 关键词：Data mining ; Cluster analysis ; Ant clustering algorithm
- 刊名：Lecture Notes in Computer Science
- 出版年：2016
- 出版时间：2016
- 年：2016
- 卷：9622
- 期：1
- 页码：117-126
- 全文大小：554 KB
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Jan Kozak (17)
17. Institute of Computer Science, University of Silesia, Sosnowiec, Poland - 丛书名：Intelligent Information and Database Systems
- ISBN：978-3-662-49390-8
- 刊物类别：Computer Science
- 刊物主题：Artificial Intelligence and Robotics
Computer Communication Networks
Software Engineering
Data Encryption
Database Management
Computation by Abstract Devices
Algorithm Analysis and Problem Complexity - 出版者：Springer Berlin / Heidelberg
- ISSN：1611-3349
文摘
In the midst of data mining tasks, clustering algorithms received special attention, especially when these techniques are bio-inspired and while they use special methods which improve a learning process during clusterization. Most promising among them are ant-based approaches. The process of clustering with colony of virtual ants is emerging and can be an alternative, when the data is complicated. Clustering, based on ant’s behavior, was first introduced by Deneubourg et al. in 1991 and this classical proposition still requires investigation to improve stability, scalability and convergence of speed. This investigations will show that we can create a mature tool for clustering. The aim of this research was to examine the execution of a new Ant Clustering Algorithm with a modified scheme of ants’ perception and an incorporation of pheromone matrices. To assess the performance of our proposition, certain amount of widely known benchmark data sets were used. Empirical study of our approach shows that the adACA performs well when the pheromone matrices influence the behavior of clustering ants and leads to better results.