MCOKE: Multi-Cluster Overlapping K-Means Extension Algorithm

Clustering involves the partitioning of n objects into k
clusters. Many clustering algorithms use hard-partitioning techniques
where each object is assigned to one cluster. In this paper we propose
an overlapping algorithm MCOKE which allows objects to belong to
one or more clusters. The algorithm is different from fuzzy clustering
techniques because objects that overlap are assigned a membership
value of 1 (one) as opposed to a fuzzy membership degree. The
algorithm is also different from other overlapping algorithms that
require a similarity threshold be defined a priori which can be
difficult to determine by novice users.

• Said Baadel
• Fadi Thabtah
• Joan Lu

• Data mining
• k-means
• MCOKE
• overlapping.

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