Evolving Efficient Clustering and Classification Patterns in Lymphography Data through Data Mining Techniques

Shomona Gracia Jacob1 and R.Geetha Ramani2, 1Rajalakshmi Engineering College, India, 2Anna University, Chennai, India

ABSTRACT

Data mining refers to the process of retrieving knowledge by discovering novel and relative patterns from large datasets. Clustering and Classification are two distinct phases in data mining that work to provide an established, proven structure from a voluminous collection of facts. A dominant area of modern-day research in the field of medical investigations includes disease prediction and malady categorization. In this paper, our focus is to analyze clusters of patient records obtained via unsupervised clustering techniques and compare the performance of classification algorithms on the clinical data. Feature selection is a supervised method that attempts to select a subset of the predictor features based on the information gain. The Lymphography dataset comprises of 18 predictor attributes and 148 instances with the class label having four distinct values. This paper highlights the accuracy of eight clustering algorithms in detecting clusters of patient records and predictor attributes and highlights the performance of sixteen classification algorithms on the Lymphography dataset that enables the classifier to accurately perform multi-class categorization of medical data. Our work asserts the fact that the Random Tree algorithm and the Quinlan’s C4.5 algorithm give 100 percent classification accuracy with all the predictor features and also with the feature subset selected by the Fisher Filtering feature selection algorithm.. It is also stated here that the Density Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm offers increased clustering accuracy in less computation time.

KEYWORDS

Data mining, Clustering, Feature Selection, Classification, Lymphography Data 

Original Source URL: https://airccse.org/journal/ijsc/papers/3312ijsc09.pdf

https://airccse.org/journal/ijsc/current2012.html

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Multispectral Image Analysis Using Random Forest

Barrett Lowe and Arun Kulkarni

Department of Computer Science, The University of Texas at Tyler

ABSTRACT

Classical methods for classification of pixels in multispectral images include supervised classifiers such as the maximum-likelihood classifier, neural network classifiers, fuzzy neural networks, support vector machines, and decision trees. Recently, there has been an increase of interest in ensemble learning – a method that generates many classifiers and aggregates their results. Breiman proposed Random Forestin 2001 for classification and clustering. Random Forest grows many decision trees for classification. To classify a new object, the input vector is run through each decision tree in the forest. Each tree gives a classification. The forest chooses the classification having the most votes. Random Forest provides a robust algorithm for classifying large datasets. The potential of Random Forest is not been explored in analyzing multispectral satellite images. To evaluate the performance of Random Forest, we classified multispectral images using various classifiers such as the maximum likelihood classifier, neural network, support vector machine (SVM), and Random Forest and compare their results.

KEYWORDS

Classification, Decision Trees, Random Forest, Multispectral Images

Original Source URL: https://airccse.org/journal/ijsc/papers/6115ijsc01.pdf

https://airccse.org/journal/ijsc/current2015.html

EVOLVING EFFICIENT CLUSTERING AND CLASSIFICATION PATTERNS IN LYMPHOGRAPHY DATA THROUGH DATA MINING TECHNIQUES

EVOLVING EFFICIENT CLUSTERING AND CLASSIFICATION PATTERNS IN LYMPHOGRAPHY DATA THROUGH DATA MINING TECHNIQUES 

Shomona Gracia Jacob1 and R.Geetha Ramani2 1Department of Computer Science and Engineering, Rajalakshmi Engineering College (Affiliated to Anna University, Chennai) graciarun@gmail.com 2Department of Information Science and Technology, College of Engineering, Guindy, Anna University, Chennai. rgeetha@yahoo.com

ABSTRACT 

Data mining refers to the process of retrieving knowledge by discovering novel and relative patterns from large datasets. Clustering and Classification are two distinct phases in data mining that work to provide an established, proven structure from a voluminous collection of facts. A dominant area of modern-day research in the field of medical investigations includes disease prediction and malady categorization. In this paper, our focus is to analyze clusters of patient records obtained via unsupervised clustering techniques and compare the performance of classification algorithms on the clinical data. Feature selection is a supervised method that attempts to select a subset of the predictor features based on the information gain. The Lymphography dataset comprises of 18 predictor attributes and 148 instances with the class label having four distinct values. This paper highlights the accuracy of eight clustering algorithms in detecting clusters of patient records and predictor attributes and highlights the performance of sixteen classification algorithms on the Lymphography dataset that enables the classifier to accurately perform multi-class categorization of medical data. Our work asserts the fact that the Random Tree algorithm and the Quinlan’s C4.5 algorithm give 100 percent classification accuracy with all the predictor features and also with the feature subset selected by the Fisher Filtering feature selection algorithm.. It is also stated here that the Density Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm offers increased clustering accuracy in less computation time. 

KEYWORDS 

Data mining, Clustering, Feature Selection, Classification, Lymphography Data   

ORIGINAL SOURCE URL : http://airccse.org/journal/ijsc/papers/3312ijsc09.pdf

http://airccse.org/journal/ijsc/current2012.html