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156 results for “Data Clustering”
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Research ArticleOpen accessIn the abstractEfficient Hybrid Network (Wired and Wireless) Intrusion Detection using Statistical Data Streams and Detection of Clustered AlertsP. Thangaraj, M. ThangavelProblem statement: Wireless LAN IEEE 802.11 protocols are growing rapidly and security has always been a concern with the security of wired network. Wireless networks encountered threats from unauthorized access to network resources, installation of access points and illegal sniffing (refer as classical intrusion threats). In its current hybrid wired and wireless network attacks on the generally distinguish from normal … …Journal of Computer Science, Vol. 7, Issue 9 · 30 Jul 2011 · 7,169 accessesView PDFCite
Research ArticleOpen accessIn the abstractImproving the Performance of Multivariate Bernoulli Model based Documents Clustering Algorithms using Transformation TechniquesPerumal Pitchandi, Nedunchezhian RajuProblem statement: Document clustering is the most important areas of data mining since they are very much and currently the subject of significant global research since such areas strengthen the enterprises of web intelligence, web mining, web search engine design and so forth. Generative models based on the multivariate Bernoulli and multinomial distributions have been widely used for text classification. … …Journal of Computer Science, Vol. 7, Issue 5 · 7 May 2011 · 7,284 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Novel Approach to Medical Image SegmentationNandagopalan Shanmugam, Adiga B. Suryanarayana, Sudarshan TSB et al.Problem statement: Segmentation is a vital aspect of medical imaging. It aids in the visualization of medical data and diagnostics of various diseases. Ultrasound image segmentation, in particular echocardiographic image segmentation, is required to identify the regions of interest such as Left Ventricle (LV) and other cardiac cavities. Existing methods do not address the drawbacks of speed and quality of … …Journal of Computer Science, Vol. 7, Issue 5 · 7 May 2011 · 7,216 accessesView PDFCite
Research ArticleOpen accessIn the abstractPredicting Missing Attribute Values Using k-Means ClusteringNambiraj Suguna, Keppana Gowder ThanushkodiProblem statement: Predicting the value for missing attributes is an important data preprocessing problem in data mining and knowledge discovery tasks. Several methods have been proposed to treat missing data and the one used more frequently is deleting instances containing at least one missing value of a feature. When the dataset has minimum number of missing attribute values then we … …Journal of Computer Science, Vol. 7, Issue 2 · 25 Feb 2011 · 7,876 accessesView PDFCite
Research ArticleOpen accessIn the abstractTone Question of Tree Based Context Clustering for Hidden Markov Model Based Thai Speech SynthesisSuphattharachai ChomphanProblem statement: In HMM-based Thai speech synthesis, tone is an important issue that brings about the intelligibility of the synthesized speech. Tone distortion resulted from imbalance of the training data should be appropriately treated. Approach: This study described an HMM-based speech synthesis system for Thai language. In the system, spectrum, pitch and state duration are modeled simultaneously in a unified … …Journal of Computer Science, Vol. 6, Issue 12 · 9 Nov 2010 · 6,823 accessesView PDFCite
Research ArticleOpen accessIn the abstractAcoustic Model Adaptation for Indonesian Language Utterance Training SystemLinda Indrayanti, Yoshifumi Chisaki, Tsuyoshi UsagawaProblem statement: In order to build an utterance training system for Indonesian language, a speech recognition system designed for Indonesian is necessary. However, the system hardly works well due to the pronunciation variants of non-native utterances may lead to substitution/deletion error. This research investigated the pronunciation variant and proposes acoustic model adaptation to improve performance of the system. Approach: The … …Journal of Computer Science, Vol. 6, Issue 11 · 23 Oct 2010 · 7,173 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Application of Session Based Clustering to Analyze Web Pages of User Interest from Web Log FilesC. P. Sumathi, R. Padmaja Valli, T. SanthanamProblem statement: With the continued growth and proliferation of e-commerce, Web services and Web-based information systems, the volumes of click-stream and user data collected by Web-based organizations in their daily operations have reached astronomical proportions. Analyzing such data can help these organizations optimize the functionality of web-based applications and provide more personalized content to visitors. This type of analysis involved … …Journal of Computer Science, Vol. 6, Issue 7 · 31 Jul 2010 · 6,879 accessesView PDFCite
Research ArticleOpen accessIn the abstractConstructing Fuzzy Time Series Model Based on Fuzzy Clustering for a ForecastingAshraf K. Abd Elaal, Hesham A. Hefny, Ashraf H. Abd ElwahabProblem statement: In this study researchers introduced a fuzzy time series model depending on fuzzy clustering to solve the problem in which the membership values are assumed as Song and Chissom model and to increase the performance of fuzzy time series model. Approach: Proposed model employed seven main procedures in time-invariant fuzzy time-series and time-variant fuzzy time series models. In … …Journal of Computer Science, Vol. 6, Issue 7 · 31 Jul 2010 · 9,751 accessesView PDFCite
Research ArticleOpen accessIn the abstractIntegrated Clustering and Feature Selection Scheme for Text Documents.M. Thangamani, P. ThangarajProblem statement: Text documents are the unstructured databases that contain raw data collection. The clustering techniques are used group up the text documents with reference to its similarity. Approach: The feature selection techniques were used to improve the efficiency and accuracy of clustering process. The feature selection was done by eliminate the redundant and irrelevant items from the text document … …Journal of Computer Science, Vol. 6, Issue 5 · 31 May 2010 · 7,165 accessesView PDFCite
Research ArticleOpen accessIn the abstractSingle Pass Seed Selection Algorithm for k-MeansK. Karteeka Pavan, Allam Appa Rao, A.V. Dattatreya Rao, G. R. SridharProblem statement: The k-means method is one of the most widely used clustering techniques for various applications. However, the k-means often converges to local optimum and the result depends on the initial seeds. Inappropriate choice of initial seeds may yield poor results. k-means++ is a way of initializing k-means by choosing initial seeds with specific probabilities. Due to the random … …Journal of Computer Science, Vol. 6, Issue 1 · 31 Jan 2010 · 7,544 accessesView PDFCite