Optimal Decision Tree Based Unsupervised Learning Method for Data Clustering
Nagarjuna Reddy Seelam, Sai Seelam, Babu Mukkala · International journal of intelligent engineering and systems · 2017
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.