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Designing efficient estimation of distribution algorithms for optimizing complex continuous problems is still a challenging task. Nowadays, histogram probabilistic model has become a hot topic in the field of estimation of distribution algorithms because of its intrinsic multimodality that makes it proper to describe the solution distribution of complex and multimodal continuous problems. To make...
Allied fuzzy c-means (AFCM) clustering is a hybrid fuzzy clustering algorithm based on the combination of fuzzy c-means (FCM) and new possibilistic c-means (NPCM). AFCM can deal with noisy data better than FCM and does not generate coincident clusters. With kernel methods AFCM is improved as its kernel learning machine model. This proposed algorithm is called kernel allied fuzzy c-means (KAFCM) clustering...
This paper describes our work on developing a language-independent technique for discovery of implicit knowledge about patents from multilingual patent information sources. Traditional techniques of multi- and cross-language patent retrieval are mostly based on the process of translation. One major problem of those is that it is difficult to find related patents produced from other countries in a...
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...
In clustering analysis, many methods require the designer to provide the number of clusters. Unfortunately, the designer has no idea, in general, about this information beforehand. In this paper, we propose a genetic algorithm based clustering method called automatic genetic clustering for unknown K (AGCUK). The AGCUK algorithm is able to automatically provide the number of clusters and find the clustering...
Research work related to plagiarism detection methods in dealing with monolingual texts (e.g. English texts) have been well established in recent years. However, little attention has been paid to facilitate plagiarism detection in cross-lingual text collections (e.g. English and Chinese texts). In this paper we present a system platform to evaluating text similarity and relatedness in multilingual...
Based on a new distance, a novel noise-resistant fuzzy clustering algorithm, called alternative noise clustering (ANC) algorithm, is proposed. ANC is an extension of the noise clustering (NC) algorithm proposed by Dave. By replacing the Euclidean distance used in the objective function of NC algorithm, a new distance (non-Euclidean distance) is introduced in NC algorithm. Based on robust statistical...
A novel fuzzy clustering algorithm, called kernel possibilistic c-means model (KPCM), is proposed. KPCM algorithm is based on kernel methods and possibilistic c-means (PCM) algorithm and it is the extension of PCM algorithm. Different from PCM and FCM which are based on Euclidean distance, the proposed model is based on kernel-induced distance by using kernel methods. Furthermore, with kernel methods...
Based on a new distance, a novel noise-resistant fuzzy clustering algorithm called alternative noise clustering (ANC) algorithm is proposed. ANC is an extension of the noise clustering (NC) algorithm proposed by Dave (1993). By replacing the Euclidean distance used in the objective function of NC algorithm, a new distance (non-Euclidean distance) is introduced in NC algorithm. Based on robust statistical...
Based on a new distance, a novel noise-resistant fuzzy clustering algorithm called alternative noise clustering (ANC) algorithm is proposed. ANC is an extension of the noise clustering (NC) algorithm proposed by Dave. By replacing the Euclidean distance used in the objective function of NC algorithm, a new distance (non-Euclidean distance) is introduced in NC algorithm. Based on robust statistical...
A fuzzy clustering method is presented based on kernel methods. The proposed model is called kernel possibilistic fuzzy c-means model (KPFCM). It is claimed that KPFCM is an extension of possibilistic fuzzy c-means model (PFCM) which is superior to fuzzy c-means (FCM) model. Different from PFCM and FCM which are based on Euclidean distance, the proposed model is based on non-Euclidean distance by...
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