A Clustering Algorithm for Data Mining Based on Swarm Intelligence
| Autor: | Peng Jin, Yunlong Zhu, Kun-Yuan Hu |
|---|---|
| Rok vydání: | 2007 |
| Předmět: |
Clustering high-dimensional data
Fuzzy clustering Computer science Computer Science::Neural and Evolutionary Computation Correlation clustering Machine learning computer.software_genre ComputingMethodologies_ARTIFICIALINTELLIGENCE Swarm intelligence CURE data clustering algorithm Consensus clustering Cluster analysis FSA-Red Algorithm business.industry k-means clustering Swarm behaviour Computer Science::Multiagent Systems ComputingMethodologies_PATTERNRECOGNITION Data stream clustering Outlier Canopy clustering algorithm Affinity propagation FLAME clustering Unsupervised learning Artificial intelligence Data mining business computer |
| Zdroj: | 2007 International Conference on Machine Learning and Cybernetics. |
| DOI: | 10.1109/icmlc.2007.4370252 |
| Popis: | Clustering analysis is an important function of data mining. Various clustering methods are need for different domains and applications. A clustering algorithm for data mining based on swarm intelligence called Ant-Cluster is proposed in this paper. Ant-Cluster algorithm introduces the concept of multi-population of ants with different speed, and adopts fixed moving times method to deal with outliers and locked ant problem. Finally, we experiment on a telecom company's customer data set with SWARM, agent-based model simulation software, which is integrated in SIMiner, a data mining software system developed by our own studies based on swarm intelligence. The results illuminate that Ant-Cluster algorithm can get clustering results effectively without giving the number of clusters and have better performance than k-means algorithm. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|