Fuzzy Clustering Using Hybrid Fuzzy C-Means and Fuzzy Particle Swarm Optimization

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Provided by: Institute of Electrical & Electronic Engineers
Topic: Big Data
Format: PDF
Clustering is the process of assigning data objects into a set of disjoint groups called clusters so that objects in each cluster are more similar to each other than objects from different clusters. Clustering techniques are applied in many application areas such as pattern recognition, data mining, machine learning, etc. Clustering algorithms can be broadly classified as hard, fuzzy, possibilistic, and probabilistic. K-means is one of the most popular hard clustering algorithms which partitions data objects into k clusters where the number of clusters, k, is decided in advance according to application purposes.
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