Urdu News Clustering Using K-Mean Algorithm On The Basis Of Jaccard Coefficient And Dice Coefficient Similarity

Clustering is the unsupervised machine learning process that group data objects into clusters such that objects within the same cluster are highly similar to one another. Every day the quantity of Urdu text is increasing at a high speed on the internet. Grouping Urdu news manually is almost impossible, and there is an utmost need to device a mechanism which cluster Urdu news documents based on their similarity. Clustering Urdu news documents with accuracy is a research issue and it can be solved by using similarity techniques i.e., Jaccard and Dice coefficient, and clustering k-mean algorithm. In this research, the Jaccard and Dice coefficient has been used to find the similarity score of Urdu News documents in python programming language. For the purpose of clustering, the similarity results have been loaded to Waikato Environment for Knowledge Analysis (WEKA), by using k-mean algorithm the Urdu news documents have been clustered into five clusters. The obtained cluster's results were evaluated in terms of Accuracy and Mean Square Error (MSE). The Accuracy and MSE of Jaccard was 85% and 44.4%, while the Accuracy and MSE of Dice coefficient was 87% and 35.76%. The experimental result shows that Dice coefficient is better as compared to Jaccard similarity on the basis of Accuracy and MSE

Clustering Complex Data Represented as propositional formulas

International audienceClustering has been extensively studied to deal with different kinds of data. Usually, datasets are represented as a n-dimensional vector of attributes described by numerical or nominal categorical values. Symbolic data is another concept where the objects are more complex such as intervals, multi-categorical or modal. However, new applications might give rise to even more complex data describing for example customer desires, constraints, and preferences. Such data can be expressed more compactly using logic-based representations. In this paper, we introduce a new clustering framework, where complex objects are described by propositional formulas. First, we extend the two well-known k-means and hierarchical agglomerative clustering techniques. Second, we introduce a new divisive algorithm for clustering objects represented explicitly by sets of models. Finally, we propose a propositional satisfiability based encoding of the problem of clustering propositional formulas without the need for an explicit representation of their models. Preliminary experimental results validating our proposed framework are provided