Clustering of Leukemia Patients via Gene Expression Data Analysis

This thesis attempts to cluster some leukemia patients described by gene expression data, and discover the most discriminating a few genes that are responsible for the clustering. A combined approach of Principal Direction Divisive Partitioning and bisect K-means algorithms is applied to the clustering of the selected leukemia dataset, and both unsupervised and supervised methods are considered in order to get the optimal results. As shown by the experimental results and the predefined reference, the combination of PDDP and bisect K-means successfully clusters the leukemia patients, and efficiently discovers some significant genes that can serve as the discriminator of the clustering. The combined approach works well on the automatic clustering of leukemia patients depending merely on the gene expression information, and it has great potential on solving similar problems. The discovered a few genes may provide very important information for the diagnosis of the disease of leukemia

Study of Clustering Data Mining Techniques

Data mining's primary purpose is to take a massive records series and wreck it down right into a more plausible form for evaluation and alertness. Exploratory facts evaluation and information mining applications frequently center on clustering. The time period "clustering" refers back to the method of categorizing facts factors into groupings wherein the objects within every cluster have more similarities than differences (clusters). Each approach serves a completely unique motive, determined by using the nature of the records at hand and the needs of the software. Nonetheless, our research has led us to the realization that the K-way approach outperforms the options in a huge type of settings. In this look at, senior undergraduate and master's degree college students from the Faculty of Economics and Business Administration at Babe?-Bolyai University of Cluj-Napoca participated via the usage of questionnaires in a collaborative effort, with the gathered data being processed through information mining clustering techniques, graphical and percent representations, the use of algorithms applied in the software program Wek

Meta-optimizations for Cluster Analysis

This dissertation thesis deals with advances in the automation of cluster analysis.This dissertation thesis deals with advances in the automation of cluster analysis

Decision Tree Induction & Clustering Techniques In SAS Enterprise Miner, SPSS Clementine, And IBM Intelligent Miner A Comparative Analysis

Decision tree induction and Clustering are two of the most prevalent data mining techniques used separately or together in many business applications. Most commercial data mining software tools provide these two techniques but few of them satisfy business needs.  There are many criteria and factors to choose the most appropriate software for a particular organization. This paper aims to provide a comparative analysis for three popular data mining software tools, which are SAS® Enterprise Miner, SPSS Clementine, and IBM DB2® Intelligent Miner based on four main criteria, which are performance, functionality, usability, and auxiliary Task Support