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Clustering in data mining is used for identifying useful patterns and interesting distributions in the underlying data. Several algorithms for clustering large data sets have been proposed in the literature using different techniques. Density-based method is one of these methodologies which can detect arbitrary shaped clusters where clusters are defined as(More)
—Most of the techniques used in the literature in clustering symbolic data are based on the hierarchical methodology, which utilizes the concept of agglomerative or divisive methods as the core of the algorithm. The main contribution of this paper is to show how to apply the concept of fuzziness on a data set of symbolic objects and how to use this concept(More)
Because of the different characteristics of Arabic language and Romance and Anglo Saxon languages, recognition of documents written in hybrid of these languages requires that the language of the text to be identified priori to the recognition phase. In this paper, three efficient techniques that can be used to discriminate between text written in Arabic(More)
This paper presents a graph-based framework for the segmentation of Arabic text. The same framework is used to extract font independent structural features from the text that are used in the recognition. The major contribution of this paper is a new graph-based structural segmentation approach based on the topological relation between the baseline and the(More)
In this paper, we present VSCAN, a novel approach for generating static video summaries. This approach is based on a modified DBSCAN clustering algorithm to summarize the video content utilizing both color and texture features of the video frames. The paper also introduces an enhanced evaluation method that depends on color and texture features. Video(More)
Several validity indices have been designed to evaluate solutions obtained by clustering algorithms. Traditional indices are generally designed to evaluate center-based clustering, where clusters are assumed to be of globular shapes with defined centers or representatives. Therefore they are not suitable to evaluate clusters of arbitrary shapes and(More)
This paper presents WhatAreYouLOOKing4 (WAY-LOOK4) system, a novel framework for content-based image retrieval (CBIR). Local descriptors are used to describe the visual contents of an image. Image signatures and similarity retrieval are based on the images' color and texture features. The main motivation of the system design is to use simple and efficient(More)
Outlier gene expression patterns identify abnormal gene behavior, possibly indicating the deviation in gene function for certain tumor types. It may also reveal novel gene-tumor relations, as well as novel tumor types. This is important in designing drugs for tumors as well as in studying the functional relations between genes. Apart from identifying(More)