![ZRL96] Tian Zhang, Raghu Ramakrishnan, and Miron Livny. Birch](http://s1.studyres.com/store/data/000747640_1-addf9ab4ee746db8d51240a091979a0d-300x300.png)
A Data Mining Approach on Cluster Analysis of IPL
... Cluster analysis is a technique which discovers the substructure of a data set by dividing it into several clusters. Clustering plays an important role in data analysis and interpretation. It has been widely used for data analysis and has been an active subject in several research fields such as sta ...
... Cluster analysis is a technique which discovers the substructure of a data set by dividing it into several clusters. Clustering plays an important role in data analysis and interpretation. It has been widely used for data analysis and has been an active subject in several research fields such as sta ...
OP-Cluster: Clustering by Tendency in High Dimensional Space
... closest matching in high dimensional spaces. Recent research work [18, 19, 3, 4, 6, 9, 12] has focused on discovering clusters embedded in the subspaces of a high dimensional data set. This problem is known as subspace clustering. Based on the measure of similarity, there are two categories of clust ...
... closest matching in high dimensional spaces. Recent research work [18, 19, 3, 4, 6, 9, 12] has focused on discovering clusters embedded in the subspaces of a high dimensional data set. This problem is known as subspace clustering. Based on the measure of similarity, there are two categories of clust ...
PDF
... extraction portion of algorithm is applied. In the first stage 3800 words are extracted from sample of documents, after preprocessing the documents, 1428 unique words and 19420 sentences are extracted. Then the algorithm constructs the Sentences Vs Words matrix for 19420 sentences as rows and 1228 w ...
... extraction portion of algorithm is applied. In the first stage 3800 words are extracted from sample of documents, after preprocessing the documents, 1428 unique words and 19420 sentences are extracted. Then the algorithm constructs the Sentences Vs Words matrix for 19420 sentences as rows and 1228 w ...
DISCOVERING PATTERNS IN DATA USING ORDINAL DATA
... 4.2. Hierarchical methods. A hierarchical method creates a hierarchical decomposition of the given set of data objects. A hierarchical method can be classified as being either agglomerative or divisive, based on how the hierarchical decomposition is formed. The agglomerative approach starts with eac ...
... 4.2. Hierarchical methods. A hierarchical method creates a hierarchical decomposition of the given set of data objects. A hierarchical method can be classified as being either agglomerative or divisive, based on how the hierarchical decomposition is formed. The agglomerative approach starts with eac ...
Nearest-neighbor chain algorithm
In the theory of cluster analysis, the nearest-neighbor chain algorithm is a method that can be used to perform several types of agglomerative hierarchical clustering, using an amount of memory that is linear in the number of points to be clustered and an amount of time linear in the number of distinct distances between pairs of points. The main idea of the algorithm is to find pairs of clusters to merge by following paths in the nearest neighbor graph of the clusters until the paths terminate in pairs of mutual nearest neighbors. The algorithm was developed and implemented in 1982 by J. P. Benzécri and J. Juan, based on earlier methods that constructed hierarchical clusterings using mutual nearest neighbor pairs without taking advantage of nearest neighbor chains.