Optimal Decision Tree Based Unsupervised Learning Method for Data Clustering
Journal Title: International Journal of Intelligent Engineering and Systems - Year 2017, Vol 10, Issue 2
Abstract
Clustering is an investigative data analysis task. It aims to find the intrinsic structure of data by organizing data objects into similarity groups or clusters. Our investigation using a pattern based clustering on numerical data set; here, we are using a Parkinson and spam dataset. These techniques are strongly related to the statistical field of cluster analysis, where over the years a large number of clustering methods has been proposed. Here, we have proposed an improved k-means clustering algorithm is used to extract patterns from a collection of an unsupervised decision tree. In our proposed research, we introduce a binary cuckoo search based decision tree. In this tree based learning technique, extracting patterns from a given dataset. Here, we have clustered the data with the aid of improved k-means clustering algorithm. The performance can be evaluated in terms of sensitivity, specificity, and accuracy.
Authors and Affiliations
Nagarjuna Seelam
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