Integrating Apriori with paired k-means for Cluster fixed mixed data
H. Haripriya, S Amrutha, R. Veena, Prema Nedungadi · 2015
The field of data mining is concerned with finding interesting patterns from an unstructured data. A simple, popular as well as an efficient clustering technique for data analysis is k-means. But classical k-means algorithm can only be applied to numerical data where k is a user given value. But the data generated from a wide variety of domains are of mixed form and it is effortful to trust on a user given value for k. So our objective is to effectively use an association rule mining algorithm which can automatically compute the number of clusters and a pairwise distance measure for calculating the distance in mixed data. We have done experimentations with real mixed data taken from the UCI repository.