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Author Al-Omary, Alauddin Yousifen_US
Author Jamil, Mohammad Shahiden_US
Available date 2009-12-30T06:10:10Zen_US
Publication Date 2005-10-04en_US
Publication Name Knowledge-Based Systems
Citation Alauddin Yousif Al-Omary, Mohammad Shahid Jamil, A new approach of clustering based machine-learning algorithm, Knowledge-Based Systems, Volume 19, Issue 4, August 2006, Pages 248-258en_US
URI http://dx.doi.org/10.1016/j.knosys.2005.10.011en_US
URI http://hdl.handle.net/10576/10579en_US
Abstract 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.en_US
Language enen_US
Publisher Elsevier B.V.en
Subject Machine learningen_US
Subject Clustering algorithmen_US
Subject Unsupervised learningen_US
Subject Evidential reasoningen_US
Subject Incremental learningen_US
Subject Multiple inheritanceen_US
Subject Overlapping concepten_US
Title A new approach of clustering based machine-learning algorithmen_US
Type Articleen_US


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