چکیده (انگلیسی):

Clustering divides the data available as bulk into meaningful,
useful groups (Clusters) without any prior knowledge about the data. Cluster
analysis provides an abstraction from individual data objects to the clusters in
which those objects reside. It is a key technique in the data mining and has
become an important issue in many fields. This paper presents a novel
Fractional Lion Algorithm (FLA) as an optimization methodology for the
clustering problems. The proposed algorithm utilizes the lion's unique
characteristics such as pride, laggardness exploitation, territorial defence and
territorial take over. The Lion algorithm is modified with the fractional theory
to search the cluster centroids. The proposed fractional lion algorithm
estimates the centroids with the systematic initialization itself. Proposed
methodology is a robust one, since the parameters utilized are insensitive and
not problem dependent. The performance of the proposed rapid centroid
estimation is evaluated using the cluster accuracy, jaccard coefficient and
rand coefficient. The quality of this approach is evaluated on the
benchmarked iris and wine data sets. On comparing with the particle swarm
clustering algorithm, experimental results shows that the clustering accuracy
of about 75% is achieved by the proposed algorithm.