The data sets generated from such studies are large and require sophisticated tools for proper analysis. In this chapter we review several techniques employed in clustering data sets of this type. Clustering can often reveal broad patterns which show that certain genes or proteins are performing ...
Clustering by pattern similarity in large data sets Clustering is the process of grouping a set of objects into classes of similar objects. Although definitions of similarity vary from one clustering model t... Wang,Haixun,Wang,... 被引量: 264发表: 2014年 Grid-Clustering: An Efficient Hierarch...
CLARA algorithm(Clustering Large Applications), which is an extension to PAM adapted for large data sets. For each of these methods, we provide: the basic idea and the key mathematical concepts the clustering algorithm and implementation in R software ...
Examples of computing and visualizing hierarchical clustering in R How to cut dendrograms into groups. How to compare two dendrograms. Solutions for handling dendrograms of large data sets. Related Book Practical Guide to Cluster Analysis in R ...
Huang, Z. Clustering large data sets with mixed numeric and categorical values, in Proceedings of the 1st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 21–34 (1997). Bair, E., Tibshirani, R. & Golub, T. Semi-supervised methods to predict patient survival from gene...
Huang Z (1997) A fast clustering algorithm to cluster very large categorical data sets in data mining. DMKD 3:34–39 Google Scholar Huang X, Ye Y, Xiong L, Lau RY, Jiang N, Wang S (2016) Time series k-means: a new k-means type smooth subspace clustering for time series data. Inf...
ClusterTree: integration of cluster representation and nearest-neighbor search for large data sets with high dimensions We introduce the ClusterTree, a new indexing approach for representing clusters generated by any existing clustering approach. A cluster is decomposed into... Dantong,Yu,Aidong,......
We propose a pragmatic and scalable version of the tight clustering method that is applicable to data sets of very large size and deduce the properties of the proposed algorithm. We validate our algorithm with extensive simulation study and multiple real data analyses including analysis of real ...
K means is generally faster than K medoids and is recommended for large data sets. The cluster number assigned to a set of features may change from one run to the next. For example, if you partition features into two clusters based on an income variable, the first time you run the ...
[HUANG97](1,2)Huang, Z.: Clustering large data sets with mixed numeric and categorical values, Proceedings of the First Pacific Asia Knowledge Discovery and Data Mining Conference, Singapore, pp. 21-34, 1997. [HUANG98]Huang, Z.: Extensions to the k-modes algorithm for clustering large data...