Hybrid data clustering approach using K-Means and Flower Pollination Algorithm
This is an incremental improvement for data clustering applications, addressing initialization issues in K-Means.
The paper tackles the problem of K-Means clustering getting trapped in local optima by proposing a hybrid approach combining K-Means with the Flower Pollination Algorithm (FPA), resulting in improved performance over both methods on eight datasets.
Data clustering is a technique for clustering set of objects into known number of groups. Several approaches are widely applied to data clustering so that objects within the clusters are similar and objects in different clusters are far away from each other. K-Means, is one of the familiar center based clustering algorithms since implementation is very easy and fast convergence. However, K-Means algorithm suffers from initialization, hence trapped in local optima. Flower Pollination Algorithm (FPA) is the global optimization technique, which avoids trapping in local optimum solution. In this paper, a novel hybrid data clustering approach using Flower Pollination Algorithm and K-Means (FPAKM) is proposed. The proposed algorithm results are compared with K-Means and FPA on eight datasets. From the experimental results, FPAKM is better than FPA and K-Means.