Data Mining and Knowledge Engineering

D. Saranya ¹, A. Bharathi ²

Affiliations
1 Coimbatore Institute of Information Technology, Tamil Nadu, IN
2 Bannari Amman Institute of Technology, Tamil Nadu, IN

Abstract

Machine learning deals with programs that learn from experience, i.e. programs that improve or adapt their performance on a certain task or group of tasks over time. The algorithm used for classification is OneR, Naive Bayes and C4.5 algorithm. This work use OneR, it is a simple classification algorithm that generates a one-level decision tree. OneR is able to infer typically simple, yet accurate, classification rules from a set of instances. This paper present Attribute Oriented Induction (AOI) has concept hierarchy as an advantage where concept hierarchy as a background knowledge which can be provided by knowledge engineers or domain experts. The experimental result shows that the proposed method of OneR with Attribute Oriented Induction program provides an accurate result by using UCI repository datasets.

Keywords

One Rule, Attribute Oriented Induction, Machine Learning Algorithm, Naive Bayes Algorithm.

Full Text

     

A. Bharathi ¹, A. M. Natarajan ²

Affiliations
1 Anna University, Coimbatore, IN
2 Bannari Amman Institute of Technology, Sathyamangalam, IN

Abstract

Clustering is one of the major tasks in data mining Clustering algorithms are based on a criterion that maximizes inter cluster distance and minimize intra cluster distance. In higher dimensional feature spaces, the performance and efficiency deteriorates to a greater extent. Large dimensions confuse the clustering algorithms and it is difficult to group similar data points becomes almost the same and is usually called as the “dimensionality curse” problem. These algorithms find a subset of dimensions by removing irrelevant and redundant dimensions on which clustering is performed. Dimensionality reduction technique such as Principal Component Analysis (PCA) is used for feature reduction. If different subsets of the points cluster well on different subspaces of the feature space, a global dimensionality reduction will fail. To overcome these problems, recent directions in research proposed to compute subspace cluster. The algorithms have two common limitations. First, they usually have problems with subspace clusters of different dimensionality. Second, they often fail to discover clusters of different shape and dimensionalities. The goal of this project is to develop new efficient and effective methods for high dimensional clustering.

Keywords

Data Mining, High Dimensional Clustering, Distance Measure.

Full Text