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Symbolic data classification is of great importance in classification of massive high dimensional data that may exist in domains such as bioinformatics and web mining. Feature values (events) of symbolic data are generally not single values, as in the classical case, but rather list of values, intervals or, more generally, distributions. This study proposes a symbolic classification algorithm that...
Human action recognition is a very active research topic in computer vision and pattern recognition. Recently, it has shown a great potential for human action recognition using the 3D depth data captured by the promising RGB-D cameras, and particularly, the Microsoft Kinect which has made high resolution real-time depth cheaply available. Several features and descriptors have been proposed for depth...
Motif detection has raised as an important task in bioinformatics. Recently, the discovery of motifs that are localized relative to a certain biological area has become an important task in many applications. For example, it is used to discover regulatory sequences beside the transcription start site and the neighborhood of known transcription factor binding sites [1]. Therefore, the idea of context...
Selecting the most discriminative genes/miRNAs has been raised as an important task in bioinformatics to enhance disease classifiers and to mitigate the dimensionality curse problem. Original feature selection methods choose genes/miRNAs based on their individual features regardless of how they perform together. Considering group features instead of individual ones provides a better view for selecting...
Mining the video data using unsupervised learning techniques can reveal important information regarding the internal visual content of large video databases. One of these information is the video summary which is a sequence of still pictures that represent the content of a video in such a way that the respective target group is rapidly provided with concise information about the content, while the...
A video summary is a sequence of still pictures that represent the content of a video in such a way that the respective target group is rapidly provided with concise information about the content, while the essential message of the original video is preserved. In this paper, we present VGRAPH, a simple yet effective video summarization approach that utilizes both color and texture features. This approach...
A number of attempts to classify cancer samples using miRNA/gene expression profiles are known in literature. However, semi-supervised learning models have only been recently introduced to exploit the huge unlabeled expression profiles in enhancing sample classification. It is important to combine both miRNA and gene expression sets as that provides more information on the characteristics of cancer...
Ensembles of classifiers were shown to provide better accuracy than single classifiers. However, the classification robustness is an important performance measure for classifiers and ensembles, besides accuracy, that should be considered. Increasing the robustness of classification systems results in reducing the probability of over-fitting. The robustness, as defined in this study, has not been studied...
While finding natural clusters in high dimensional data is in itself a challenge, the dynamic nature of data adds another greater challenge. Many applications such as Data Warehouses and WWW demand the presence of efficient incremental clustering algorithms to handle their dynamic data. So far, numerous useful incremental clustering algorithms have been developed for large datasets such as incremental...
Protein sequence clustering is a process that aims to identify sets of homologous proteins in a protein database. In this paper, two efficient soft c-mediods clustering algorithms for prototype selection for protein sequences are presented. In the proposed techniques patterns are considered to belong to some but not necessarily all clusters. The proposed algorithms is comprised of a judicious integration...
Ensembles of classifiers have recently proved their efficiency in cancer diagnosis based on microarray datasets. The main performance indicators, namely, accuracy and diversity, present the main focus of study when designing an ensemble. One other important performance indicator is classification robustness. In an attempt to improve the performance of an ensemble, the proposed algorithm presents a...
Document clustering has become inevitable for applications that aim to extract information from huge corpuses. Such applications face two main challenges; one is the efficient representation of the documents, along with using an efficient similarity measure, and the second is dealing with the dynamic nature of the corpus. In this paper, an efficient document clustering model is introduced for incrementally...
Due to the dramatic increase of data volumes in different applications, it is becoming infeasible to keep these data in one centralized machine. It is becoming more and more natural to deal with distributed databases and networks. That is why distributed data mining techniques have been introduced. One of the most important data mining problems is data clustering. While many clustering algorithms...
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