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Clustering of multi-view relational data based on particle swarm optimization
- Source :
- Expert Systems with Applications. 123:34-53
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- Clustering of multi-view data has received increasing attention since it explores multiple views of data sets aiming at improving clustering accuracy. Particle Swarm Optimization (PSO) is a well-known population-based meta-heuristic successfully used in cluster analysis. This paper introduces two hybrid clustering methods for multi-view relational data. These hybrid methods combine PSO and hard clustering algorithms based on multiple dissimilarity matrices. These methods take advantage of the global convergence ability of PSO and the local exploitation of hard clustering algorithms in the position update step, aiming to improve the balance between exploitation and exploration processes. Moreover, the paper provides adapted versions of 11 fitness functions suitable for vector data aiming at dealing with multi-view relational data. Two performance criteria were used to evaluate the clustering quality using the two proposed methods over eleven real-world data sets including image and document data sets. Among new findings, it was observed that the top three fitness functions are Silhouette index, Xu index and Intra-cluster homogeneity. The performance of the proposed algorithms was compared with previous single and multi-view relational clustering algorithms. The results show that the proposed methods significantly outperformed the other algorithms in the majority of cases. The results reinforce the importance of the application of techniques such as PSO-based clustering algorithms in the field of expert systems and machine learning. Such application enhances classification accuracy and cluster compactness. Besides, the proposed algorithms can be useful tools in content-based image retrieval systems, providing good categorizations and automatically learning relevance weights for each cluster of images and sets of views.
- Subjects :
- 0209 industrial biotechnology
education.field_of_study
Computer science
Relational database
Population
General Engineering
Particle swarm optimization
02 engineering and technology
computer.software_genre
Expert system
Computer Science Applications
Data set
020901 industrial engineering & automation
Compact space
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Data mining
education
Cluster analysis
computer
Image retrieval
Subjects
Details
- ISSN :
- 09574174
- Volume :
- 123
- Database :
- OpenAIRE
- Journal :
- Expert Systems with Applications
- Accession number :
- edsair.doi...........fee6f38fffccf690846a256bfd138790