YÖK Akademik
Permanent URI for this collectionhttps://acikarsiv.thk.edu.tr/handle/123456789/2553
Browse
3 results
Search Results
Publication New classification quality estimators for analysis of documentary information: Application to patent analysis and web mapping(Springer Science and Business Media LLC, 2004) Jean-Charles Lamirel; Claire Francois; Shadi Al Shehabi; Martial HoffmannPublication Using a compound approach based on elaborated neural network for Webometrics: An example issued from the EICSTES project(Springer Science and Business Media LLC, 2004) Jean-Charles Lamirel; Shadi Al Shehabi; Claire Francois; Xavier PolancoPublication MIGR: A Categorical Data Clustering Algorithm Based on Information Gain in Rough Set Theory(World Scientific Pub Co Pte Ltd, 2022-10) Saddam Raheem; Shadi Al Shehabi; Amaal Mohi NassiefClustering techniques are used to split data into clusters where each cluster contains elements that look more similar to elements in the same cluster than elements in other clusters. Some of these techniques are capable of handling clustering process uncertainty, while other techniques may have stability issues. In this paper, a novel method, called Minimum Information Gain Roughness (MIGR), is proposed to select the clustering attribute based on information entropy with rough set theory. To evaluate its performance, three benchmark UCI datasets are chosen to be clustered by using MIGR. Then, the resulting clusters are compared to those which are resulted from applying Min-Min-Rough (MMR) and information-theoretic dependency roughness (ITDR) algorithms. Both last-mentioned techniques were already compared with a variety of clustering algorithms like k-modes, fuzzy centroids, and fuzzy k-modes. The Global purity, the overall purity, and F-measure are considered here as performance measures to compare the quality of the resulting clusters. The experimental results show that the MIGR algorithm outperforms both MMR and ITDR algorithms for clustering categorical data.