High-performance link-based cluster ensemble approach for categorical data clustering

In recent years, the clustering ensembles emerged as a problem solver for extracting
the data points into clusters in an efficientway. However, still clustering poses a serious
issue due to the presence of imperfect informationwhile partitioning the data into clusters.
This creates a serious issue in creating an efficient cluster with cluster ensembles.
In this paper, we propose a solution to solve the degradation in clustering during data
partitioning. The initial clusters are generated using firefly algorithm. A linked cluster
ensemble approach uses similarity measurement using multi-viewpoint and weighted
triple quality using entropy measurements that ensembles the data points into clusters.
This avoids the problem of local optimum and avoids the issues arise from highdimensional
datasets and improve the quality of clustering. Here, the data partitioning
is done with bipartite spectral algorithm and similarity measurement. Finally, the artificial
neural network is used to generate classified results from the optimized clustered
datasets. The experimental results are carried out over UCI repository datasets and the
results show that the proposed method attains an effective ensemble clustering with
higher clustering accuracy than the conventional ones.