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 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 · 19,451 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
Research ArticleOpen accessIn the abstractDiscretization Numerical Data for Relational Data with One-to-Many RelationsRayner AlfredProblem statement: Handling numerical data stored in a relational database has been performed differently from handling those numerical data stored in a single table due to the multiple occurrences (one-to-many association) of an individual record in the non-target table and non-determinate relations between tables. Numbers in Multi-Relational Data Mining (MRDM) were often discretized, after considering the schema of the relational … …Journal of Computer Science, Vol. 5, Issue 7 · 31 Jul 2009 · 7,031 accessesView PDFCite
Research ArticleOpen accessIn the abstractNew Efficient Strategy to Accelerate k-Means Clustering AlgorithmMoh’d Belal Al- Zoubi, Amjad Hudaib, Ammar Huneiti, Bassam HammoOne of the most popular clustering techniques is the k-means clustering algorithm. However, the utilization of the k-means is severely limited by its high computational complexity. In this study, we propose a new strategy to accelerate the k-means clustering algorithm through the Partial Distance (PD) logic. The proposed strategy avoids many unnecessary distance calculations by applying efficient PD strategy. Experiments … …American Journal of Applied Sciences, Vol. 5, Issue 9 · 30 Sep 2008 · 7,440 accessesView PDFCite
Research ArticleOpen accessIn the abstractSelf-Organizing Map and Multi-Layer Perceptron Neural Network Based Data Mining To Envisage Agriculture CultivationE. T. Venkatesh, Dr. P. ThangarajStudy on characteristics of soil, to determine the types of crops suitable for cultivation in a particular region can increase the yield to greater extent, which minimizes the expenditures involved in irrigation and application of fertilizers. With the tested techniques available for calibrating the quality of soil and the crops suitable for cultivation in it, it is possible to determine … …Journal of Computer Science, Vol. 4, Issue 6 · 30 Jun 2008 · 6,789 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Effective Data Transformation Approach for Privacy Preserving ClusteringR. R. Rajalaxmi, A. M. NatarajanA new stream of research privacy preserving data mining emerged due to the recent advances in data mining, Internet and security technologies. Data sharing among organizations considered to be useful which offer mutual benefit for business growth. Preserving the privacy of shared data for clustering was considered as the most challenging problem. To overcome the problem, the data owner published … …Journal of Computer Science, Vol. 4, Issue 4 · 30 Apr 2008 · 7,117 accessesView PDFCite
Research ArticleOpen accessIn the abstractPerformance Analysis of Clustering Protocols in Mobile Ad hoc NetworksSharmila J. Francis, Elijah B. RajsinghNetwork clustering is an important technique widely used in efficient MANETs network management, hierarchical routing protocol design, network modeling, Quality of Service, etc. Recently many researchers are focusing on clustering which is one of the fundamental problems in mobile ad hoc networks. This article presents the descriptions of recently proposed clustering algorithms and is categorized into different approaches that support … …Journal of Computer Science, Vol. 4, Issue 3 · 31 Mar 2008 · 6,950 accessesView PDFCite
Research ArticleOpen accessIn the abstractDynamic Head Cluster Election Algorithm for Clustered Ad-Hoc NetworksArwa Zabian, Ahmed Ibrahim, Fadi Al-KalaniIn distributed system, the concept of clustering consists on dividing the geographical area covered by a set of nodes into small zones. In mobile network, the clustering mechanism varied due to the mobility of the nodes any time in any direction. That causes the partitioning of the network or the joining of nodes. Several existing centralized or globalized algorithm have … …Journal of Computer Science, Vol. 4, Issue 1 · 31 Jan 2008 · 7,437 accessesView PDFCite
Research ArticleOpen accessIn the abstractTaxonomically Clustering Organisms Based on the Profiles of Gene Sequences Using PCAE. Ramaraj, M. PunithavalliThe biological implications of bioinformatics can already be seen in various implementations. Biological taxonomy may seem like a simple science in which the biologists merely observe similarities among organisms and construct classifications according to those similarities[1], but it is not so simple. By applying data mining techniques on gene sequence database we can cluster the data to find interesting similarities … …Journal of Computer Science, Vol. 2, Issue 3 · 31 Mar 2006 · 6,753 accessesView PDFCite