Representative Clustering of Uncertain Data
... This paper targets the problem of deriving a meaningful clustering from an uncertain dataset. For this purpose, our aim is not to develop a new clustering algorithm, but rather to allow clustering algorithms designed for certain data to return meaningful, reliable and correct results in the presence ...
... This paper targets the problem of deriving a meaningful clustering from an uncertain dataset. For this purpose, our aim is not to develop a new clustering algorithm, but rather to allow clustering algorithms designed for certain data to return meaningful, reliable and correct results in the presence ...
Local Semantic Kernels for Text Document Clustering
... one term from the dictionary of all the words that appear in the corpus. VSM, although simple and commonly used, suffers from a number of deficiencies. Inherent shortages of VSM include breaking multi-word expressions, like Machine Learning, into independent features, mapping synonymous words into d ...
... one term from the dictionary of all the words that appear in the corpus. VSM, although simple and commonly used, suffers from a number of deficiencies. Inherent shortages of VSM include breaking multi-word expressions, like Machine Learning, into independent features, mapping synonymous words into d ...
Insights to Existing Techniques of Subspace Clustering in High
... In the present era of digitization, majority of the users are continually moving on the pervasive computing that has significantly influenced the telecommunication section and social networking section. Owing to advancement in the communication technology, now storage is not a big problem as the dat ...
... In the present era of digitization, majority of the users are continually moving on the pervasive computing that has significantly influenced the telecommunication section and social networking section. Owing to advancement in the communication technology, now storage is not a big problem as the dat ...
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.