Simultaneous Clustering and Visualization of Web Usage Data Using Swarm-Based Intelligence
Esin Saka, Olfa Nasraoui · 2008
In this paper, we use a flock of agent-based swarm intelligence approach for simultaneously clustering and visualizing high-dimensional Web usage data. Our approach is based on improvements that overcome several limitations of the FClust algorithm. Our proposed approach is a hybrid, combining the strengths of the spherical k-means algorithm for fast clustering of high-dimensional data sets in the original feature domain and the flock-based (FClust) algorithm which iteratively adjusts the position and speed of dynamic flocks of agents on a visualization plane. Our hybridization decreases the complexity of FClust from quadratic to linear, with further improvements in the cluster quality. Experiments on real data illustrate the workings of the hybrid algorithm and its advantages over its FClust baseline.