| Title | A Swarm Intelligence Approach to Avoid Local Optima in Fuzzy C-Means Clustering |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Fuchs, Caro, Spolaor, Simone, Nobile, Marco S., Kaymak, Uzay |
| Conference Name | 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) |
| Keywords | Clustering algorithms, clustering analysis, composability, compositionality, computational complexity, convergence, FST-PSO, fuzzy c-means clustering, fuzzy clustering task, Fuzzy logic, fuzzy self-tuning particle swarm optimization, fuzzy set theory, hill climbing method, inter-cluster similarity, Linear programming, NP-hard, Optimization, optimization process, particle swarm optimisation, particle swarm optimization, particle swarm optimization algorithm, Partitioning algorithms, pattern clustering, pubcrawl, swarm intelligence, swarm intelligence global optimization method, Task Analysis |
| Abstract | Clustering analysis is an important computational task that has applications in many domains. One of the most popular algorithms to solve the clustering problem is fuzzy c-means, which exploits notions from fuzzy logic to provide a smooth partitioning of the data into classes, allowing the possibility of multiple membership for each data sample. The fuzzy c-means algorithm is based on the optimization of a partitioning function, which minimizes inter-cluster similarity. This optimization problem is known to be NP-hard and it is generally tackled using a hill climbing method, a local optimizer that provides acceptable but sub-optimal solutions, since it is sensitive to initialization and tends to get stuck in local optima. In this work we propose an alternative approach based on the swarm intelligence global optimization method Fuzzy Self-Tuning Particle Swarm Optimization (FST-PSO). We solve the fuzzy clustering task by optimizing fuzzy c-means' partitioning function using FST-PSO. We show that this population-based metaheuristics is more effective than hill climbing, providing high quality solutions with the cost of an additional computational complexity. It is noteworthy that, since this particle swarm optimization algorithm is self-tuning, the user does not have to specify additional hyperparameters for the optimization process. |
| DOI | 10.1109/FUZZ-IEEE.2019.8858940 |
| Citation Key | fuchs_swarm_2019 |
A Swarm Intelligence Approach to Avoid Local Optima in Fuzzy C-Means Clustering