A new approach of clustering based machine-learning algorithm

被引:17
作者
Al-Omary, Alauddin Yousif [1 ]
Jamil, Mohammad Shahid
机构
[1] Univ Bahrain, Coll Informat Technol, Dept Comp Engn, Isa Town, Bahrain
[2] Qatar Univ, Dept Math & Comp, Fdn Program Unit, Doha, Qatar
关键词
machine learning; clustering algorithm; unsupervised learning; evidential reasoning; incremental learning; multiple inheritance; overlapping concept;
D O I
10.1016/j.knosys.2005.10.011
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Machine-learning research is to study and apply the computer modeling of learning processes in their multiple manifestations, which facilitate the development of intelligent system. In this paper, we have introduced a clustering based machine-learning algorithm called clustering algorithm system (CAS). 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 is 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. (c) 2006 Published by Elsevier B.V.
引用
收藏
页码:248 / 258
页数:11
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