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77 results for “mining algorithms”
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Research ArticleOpen accessIn the abstractEvaluating the Strengths and Weaknesses of Mining Audit Data for Automated Models for Intrusion Detection in Tcpdump and Basic Security Module DataG. Mohammed Nazer, A. Arul Lawrence SelvakumarProblem statement: Intrusion Detection System (IDS) have become an important component of infrastructure protection mechanism to secure the current and emerging networks, its services and applications by detecting, alerting and taking necessary actions against the malicious activities. The network size, technology diversities and security policies make networks more challenging and hence there is a requirement for IDS which should be … …Journal of Computer Science, Vol. 8, Issue 10 · 25 Aug 2012 · 7,126 accessesView PDFCite
Research ArticleOpen accessIn the abstractPrivacy Preserved Collaborative Secure Multiparty Data MiningM. Balamurugan, J. Bhuvana, S. Chenthur PandianProblem statement: In the current modern business environment, its success is defined by collaboration, team efforts and partnership, rather than lonely spectacular individual efforts in isolation. So the collaboration becomes especially important because of the mutual benefit it brings. Sometimes, such collaboration even occurs among competitors, or among companies that have conflict of interests, but the collaborators are aware that … …Journal of Computer Science, Vol. 8, Issue 6 · 16 Mar 2012 · 7,161 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Density Based Dynamic Data Clustering Algorithm based on Incremental DatasetS. Angel Latha Mary, K.R. Shankar KumarProblem statement: Clustering and visualizing high-dimensional dynamic data is a challenging problem. Most of the existing clustering algorithms are based on the static statistical relationship among data. Dynamic clustering is a mechanism to adopt and discover clusters in real time environments. There are many applications such as incremental data mining in data warehousing applications, sensor network, which relies on dynamic … …Journal of Computer Science, Vol. 8, Issue 5 · 20 Feb 2012 · 7,465 accessesView PDFCite
Research ArticleOpen accessIn the abstractSteel Plates Faults Diagnosis with Data Mining ModelsMahmoud Fakhr, Alaa M. ElsayadProblem statement: Over the last two decades, Fault Diagnosis (FD) has a major importance to enhance the quality of manufacturing and to lessen the cost of product testing. Actually, quick and correct FD system helps to keep away from product quality problems and facilitates precautionary maintenance. FD may be considered as a pattern recognition problem. It has been gaining more … …Journal of Computer Science, Vol. 8, Issue 4 · 31 Jan 2012 · 11,378 accessesView PDFCite
Research ArticleOpen accessIn the abstractA New Cooperative Algorithm Based on PSO and K-Means for Data ClusteringMehdi Neshat, Shima Farshchian Yazdi, Daneyal Yazdani, Mehdi SargolzaeiProblem statement: Data clustering has been applied in multiple fields such as machine learning, data mining, wireless sensor networks and pattern recognition. One of the most famous clustering approaches is K-means which effectively has been used in many clustering problems, but this algorithm has some drawbacks such as local optimal convergence and sensitivity to initial points. Approach: Particle Swarm Optimization … …Journal of Computer Science, Vol. 8, Issue 2 · 19 Nov 2011 · 7,967 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Cluster Feature-Based Incremental Clustering Approach to Mixed DataA. M. Sowjanya, M. ShashiProblem statement: The main objective of this study is to develop an incremental clustering algorithm that can handle numerical as well as categorical attributes in a given dataset. The authors have previously reported a cluster feature-based algorithm, CFICA that can handle only numerical data. Appraoch: Since many of the real life data mining applications work with datasets that contain both … …Journal of Computer Science, Vol. 7, Issue 12 · 21 Oct 2011 · 7,159 accessesView PDFCite
Research ArticleOpen accessIn the abstractFast Algorithms for Discovering Sequential Patterns in Massive DatasetsS. Dharani, Justus Rabi, Nanda Kumar, DarlyProblem statement: Sequential pattern mining is one of the specific data mining tasks, particularly from retail data. The task is to discover all sequential patterns with a user-specified minimum support, where support of a pattern is the number of data-sequences that contain the pattern. Approach: To find a sequence patterns variety of algorithm like AprioriAll and Generalized Sequential Patterns (GSP) … …Journal of Computer Science, Vol. 7, Issue 9 · 23 Jul 2011 · 7,280 accessesView PDFCite
Research ArticleOpen accessIn the abstractClassifying Single Trail Electroencephalogram Using Gaussian Smoothened Fast Hartley Transform for Brain Computer Interface during Motor ImageryV. B. Deepa, P. Thangaraj, S. ChitraProblem statement: Brain-Computer Interface (BCI) is a emerging research area which translates the brain signals for any motor related actions into computer understandable signals by capturing the signal, processing the signal and classifying the motor imagery. This area of work finds various applications in neuroprosthetics. Mental activity leads to changes of electrophysiological signals like the Electroencephalogram (EEG) or Electrocorticogram (ECoG). … …Journal of Computer Science, Vol. 7, Issue 5 · 7 May 2011 · 6,658 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Independent Rough Set Approach Hybrid with Artificial Bee Colony Algorithm for Dimensionality ReductionNambiraj Suguna, Keppana Gowder ThanushkodiProblem statement: Dimensionality reduction is viewed as an important pre-processing step for pattern recognition and data mining. As the classical rough set model considers the entire attribute set as a whole to find the subset, comparing all possible combinations of sets of attributes is difficult. Approach: In this study, we have introduced an improved Rough Set-based Attribute Reduction (RSAR) namely … …American Journal of Applied Sciences, Vol. 8, Issue 3 · 25 Mar 2011 · 8,334 accessesView PDFCite
Research ArticleOpen accessIn the abstractComputational Complexity between K-Means and K-Medoids Clustering Algorithms for Normal and Uniform Distributions of Data PointsT. Velmurugan, T. SanthanamProblem statement: Clustering is one of the most important research areas in the field of data mining. Clustering means creating groups of objects based on their features in such a way that the objects belonging to the same groups are similar and those belonging to different groups are dissimilar. Clustering is an unsupervised learning technique. The main advantage of clustering … …Journal of Computer Science, Vol. 6, Issue 3 · 31 Mar 2010 · 19,434 accessesView PDFCite