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This paper addresses the problem of cluster characterization by selecting a subset of the most relevant features for each cluster from a categorical dataset in an autonomous way. The proposed autonomous model is based on the Relational Topological Clustering (RTC) associated with a statistical test which allows to detect the most important variables in an automatic way without setting any parameters...
Feature selection is a process which chooses a subset from the original feature set according to some rules. The selected feature retains original physical meaning and provides a better understanding for the data and learning process. However, few modern feature selection approaches take the advantage of features' context information. Based on this analysis, we propose a novel feature selection method...
Extracting useful information from user generated text on the web is an important ongoing research in natural language processing, machine learning, and data mining. Online tools like emails, news groups, blogs, and web forums provide an effective communication platform for millions of users around the globe and also provide an added advantage of anonymity. Millions of people post information on different...
In this paper, we describe a two-stage hybrid approach to select gene features and produce dominant patterns for evaluating the pathological probability. To discover suitable genes as experiment samples for distinguishing the status of gene regulation, we utilized receiver operating characteristic (ROC) method to eliminate non-significant genes of unapparent variation between normal tissues and tumors...
The content-based image retrieval (CBIR) system aims at searching and browsing the large image digital libraries based on automatically derived imagery features. This paper introduces two algorithms based on the normalized cut for images clustering. We extract the color and texture features for computing the distance between the images, and take advantage of the bipartition method and minimum spanning...
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