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With the rapid advances in digital technology, the multimedia documents have been growing ubiquitously. The analysis of this huge repository of multimedia documents requires efficient organization of documents. Multimedia document clustering organizes the multimedia documents with common multimedia topics. The important step of multimedia document clustering is computing the similarity between multimedia...
Identifying families of malware is today considered a fundamental problem in the context of computer security. The correct mapping of a malicious sample to a known family simplifies its analysis and allows experts to focus their efforts only on those samples presenting unknown characteristics or behaviours, thus improving the efficiency of the malware analysis process. Grouping malware in families...
Over the past few years, the dimensionality of functional MRI (fMRI) effects the analysis of brain data. In the field of machine learning and statistical analysis, classification of objects plays a significant role. Machine learning classifiers are used to discover the class of new data points from a set of data points. The application of learning techniques on fMRI data alleviates to cognitive state...
Clustering is a well-recognized data mining technique which enables the determination of underlying patterns in datasets. In electric power systems, it has been traditionally utilized for different purposes like defining customer load profiles, tariff designs and improving load forecasting. Some surveys summarized different clustering techniques which were traditionally used for customer segmentation...
Development of smart cities has grasped much attention in research community and industry as well. Smart healthcare, communication, infrastructure are required for the development of smart cities. Security is one of the major concern in the development of smart cities. Automatic surveillance helps in boosting security in multiple areas like traffic, hospitals, schools, and industries etc. Video camera...
Search systems that is used to search for information. Cite Seer was a search engine to search academic documents. Platforms are not available to discover algorithms in scholarly big data. The limitations of these search engines make the searching more difficult. Hence special purpose systems are used. Here proposes a search system to extract algorithm representations. Algorithms can be represented...
Aspect extraction is one of the most important tasks for text mining. Semi-supervised methods have been proposed to solve this problem. However, the seed terms have to be given in advance in these methods. The current methods categorize the aspects without expanding more aspects terms. And most of the methods are based on English corpus, there is a great space for the research on the aspect extraction...
In this work we derive a novel clustering scheme for hyperspectral pixels according to the material they sense. We utilize statistical correlations that pixels sensing the same material exhibit. Specifically, kernel learning is combined with a norm-one regularized canonical correlations framework that can perform data clustering on nonlinearly dependent data. To tackle the derived minimization formulation...
An approach for detection and segmentation of individual buildings on space images and aerial photos is proposed. The approach allows intuitively constructing the system of rules to select objects without prior training, using only simple geometric characteristics of their form.
Due to the rapid growth in both the number and diversity of Web services on the web, it becomes increasingly difficult for us to find the desired and appropriate Web services nowadays. Clustering Web services according to their functionalities becomes an efficient way to facilitate the Web services discovery as well as the services management. Existing methods for Web services clustering mostly focus...
Collecting ground truth data with smart phone applications is as difficult as important for training classification models predicting transport modes of people. Errors of respondent input with respect to trip length and transport mode segmenting introduce a systematic bias in the classification model. We propose a semi-supervised framework adjusting user-given input to process user-collected accelerometer...
In this paper, a new method of saliency-based traffic sign detection is presented. On the basis of the visual attention mechanism model, edge and color information are extracted as early visual features, and each feature is computed and normalized to obtain feature maps, conspicuity maps and the saliency map. Then the candidate regions containing traffic signs are determined with self-organizing map...
This paper demonstrates a comparative study of Arabic Multi-Document Summarization System (AMD-SS). These methods are compared and analyzed, aiming to detect which method generates a genuine summary and achieves the best results in comparison with the human summarization techniques. The comparative study shows that there is a lack in the area of Arabic Automatic Text Summarization systems. Therefore,...
In this digital world, we are facing the flood of data, but depriving for knowledge. The eminent need of mining is useful to extract the hidden pattern from the wide availability of vast amount of data. Clustering is one such useful mining tool to handle this unfavorable situation by carrying out crucial steps refers as cluster analysis. It is the process of a grouping of patterns into clusters based...
Micro array data play a vital role in simultaneously monitoring the expression profile of large number of genes that are specified with various experimental conditions. In bioinformatics research, the recognition of co-expressed and coherent patterns is a major objective in micro array data analysis. The K-means clustering algorithm is gaining popularity in the knowledge discovery domain for effectively...
Text mining discover and extract useful information from documents, whenever increase the size and number documents leads to redouble features. The huge features for the documents adds challenge to text mining called high dimension. The aim of this proposed study is minimize the high dimension of the documents, and improve Arabic text mining using clustering. In order to achieve this goal, we propose...
In this paper, an exact and proactive technique is created to distinguish Distributed Denial of Service (DDoS) attacks. This is achieved by using an entropy concept to measure abnormal traffic changes according to the phases of the attack. This traffic is then clustered by using a modified DBSCAN algorithm, and the centroids for the resulting clusters are then used as patterns for efficient distance-based...
In this paper, multimedia data's are extracted using clustering technique with help of image feature values. The planned frameworks works based on image indexing and retrieval technique it replace a presented object with the retrieval result in real time. Along with this indexing mechanism a histogram-based color descriptors also introduced to reliably capture and represent the color properties of...
Clustering is one of the most common unsupervised learning tasks in machine learning and data mining. Clustering algorithms have been used in a plethora of applications across several scientific fields. However, there has been limited research in the clustering of point patterns - sets or multi-sets of unordered elements - that are found in numerous applications and data sources. In this paper, we...
Domain adaption tends to transfer knowledge across domains following dissimilar distribution and where target domain has inadequate labelled samples. When knowledge is transferred from abundantly irrelevant sources negative transfer may occur resulting in poor classification of test samples. Deep learning research illustrates the semantic clustering as well as transferability of deep convolutional...
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