Mon premier blog

Finding Groups in Data: An Introduction to Cluster Analysis Leonard Kaufman, Peter J. Rousseeuw
Publisher: Wiley-Interscience

Kogan J., Nicholas C., Teboulle M. The techniques of global partitioning of the data, such as K-means, partitioning around medoids, various flavors of hierarchical clustering, and self-organized maps [1-4], have provided the initial picture of similarity in the gene expression profiles, Another approach to finding functionally relevant groups of genes is network derivation, which has been popular in the analysis of gene-gene and protein-protein interactions [6-10], and is also applicable to gene expression analysis [11,12]. It addresses the following general problem: given a set of entities, find subsets, or clusters, which are homogeneous and/or well separated (cf. Kaufman L, Rousseeuw PJ: Finding Groups in Data: An Introduction to Cluster Analysis. Complete code of six stand-alone Fortran programs for cluster analysis, described and illustrated in L. Clustering Large and High Dimensional data. Jolliffe IT: Principal Component Analysis. Rousseeuw (1990), "Finding Groups in Data: an Introduction to Cluster Analysis" , Wiley. Finding Groups in Data: An Introduction to Cluster Analysis. Ling nice take on the 3 V's of Big Data and introducing Veracity, Value and Victory. Cluster analysis or clustering is the task of assigning a set of objects into groups (called clusters) so that the objects in the same cluster are more similar (in some sense or another) to each other than to those in other clusters. €� John Wiley & Sons, 1990 Collective Intelligence. Clustering is a main task of explorative data mining, and a common technique for statistical data analysis used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. Hoboken, NJ: John Wiley & Sons, Inc; 1990:1986. Segmentation dynamically group data into different clusters based predefined measurement like distance method. Clustering is a powerful tool for automated analysis of data. While much around big data remains hype, many companies are in the fledging stages of drawing value from their big data corpus, and given an army of discussions and opinions around the topic, it's still hard to find a clear roadmap to arrive at the Big Promise.