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Optimal Decision Tree Based Unsupervised Learning Method for Data Clustering


Nagarjuna Reddy Seelam1*, Sai Satyanaryana Reddy Seelam2, Babu Reddy Mukkala3


1Jawaharlal Nehru Technological University, Kakinada, Andhra Pradesh, India
2Vardhaman College of Engineering, Hyderabad, Telangana, India
3Krishna University, MachiliPatnam, Andhra Pradesh, India


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.


K-means clustering, Binary cuckoo search, Sensitivity, Specificity, Accuracy, Pattern.

Full Text:

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