The Evaluation and Comparative Study with a New Clustered Based Machine Learning Algorithm

Alauddin Alomary¹ and Mohammad Jamil²

¹Department of Computer Engineering, University of Bahrain, Bahrain

²Department of Math and Computer, Qatar University, Qatar

Abstract: In this paper, a clustering based machine learning algorithm called Clustering Algorithm System (CAS) is introduced. The CAS algorithm is tested to evaluate its performance and find fruitful results. We have been presented some heuristics to facilitate machine-learning authors to boost up their research works. The InfoBase of the Ministry of Civil Services is used to analyze the CAS algorithm. The CAS algorithm was compared with other machine learning algorithms like UNIMEM, COBWEB, and CLASSIT and was found to have some strong points over them. The proposed algorithm combined advantages of two different approaches to machine learning. The first approach is learning from examples, CAS supports single and multiple inheritance and exceptions. CAS also avoids probability assumptions which are well understood in concept formation. The second approach is learning by observation. CAS applies a set of operators that have proven to be effective in conceptual clustering. We have shown how CAS builds and searches through a clusters hierarchy to incorporate or characterize an object.

Keywords: Clustering algorithm, unsupervised learning, evidential reasoning, incremental learning, multiple inheritances, overlapping concept.