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
Keywords
Related Articles
- EP ID EP229416
- DOI 10.22266/ijies2017.0430.14
- Views 91
- Downloads 0
TCP M-Start: A New Slow Start Method of TCP to Transfer Data Over Long Fat Pipe Network
Transmission control protocol have gone through various revisions to develop new method of responding to network congestion control as per past, present and future requirements. In cloud computing moving large size of a...
Optimal Test Case Prioritization in Cloud based Regression Testing with Aid of KFCM
Regression testing is a kind of software testing that authenticates that software previously developed and established still accomplishes correctly after it was altered or interfaced with other software. The aim of the i...
Automatic Detection and Classification of Masses in Digital Mammograms
Breast Cancer is still one of the leading cancers in women. Mammography is the best tool for early detection of breast cancer. In this work methods for automatic detection and classification of masses into benign or mali...
Multi Agent Based Diabetes Diagnosing and Classification with the Aid of Hybrid Firefly-Neural Network
A multi agent distributed data mining system for diagnosing diabetes and classification is proposed. Here we are introducing four agents namely user agent, connection agent, updation agent, and security agent. In which e...
Appropriate Channel Selection for Dynamic Resource Allocation with Priority Scheduling Approach in Multi-hop Cognitive Radio Networks
Today’s wireless communication systems required groundbreaking solution to meet scarcity of radio spectrum. Cognitive radio (CR) is an emerging technology which provides futuristic solution and improving the precious lik...