source code for clustering with multi viewpoint based similarity measure in java
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[b] [/b]Hi am Mohamed i would like to get details on source code for clustering with multi viewpoint based similarity measure in java ..My friend Justin said source code for clustering with multi viewpoint based similarity measure in java will be available here and now i am living at ......... and i last studied in the college/school ......... and now am doing ....i need help on ......etc
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#2

All clustering methods have to assume some cluster relationship between the data objects on which they are applied. The similarity between a pair of objects can be defined explicitly or implicitly. In this article, we introduce a new measure of similarity based on multiple points of view and two related grouping methods. The main difference between a measure of traditional dissimilitude / similarity and ours is that the former uses only a single point of view, which is the origin, while the latter uses many different points of view, which are objects not found in The same group with the two objects is being measured. Using multiple points of view, a more informative assessment of similarity could be achieved. The theoretical analysis and the empirical study are carried out to support this assertion. Two criteria functions are proposed for the grouping of documents based on this new measure. We compare them with several well known clustering algorithms that use other similarity measures popular in several document collections to verify the advantages.


The main purpose of this Clustering with Multi-Viewpoint Based Similarity Measure Java Project is to give efficient phrase matching. This system works with several algorithms that are hierarchical algorithms, agglomerative algorithms, divisive algorithms and partial algorithms.

The current architecture of the project begins with web documents that can be identified by the document structures and will be well represented in the index chart of the document. The similarity of documents can be made at the end of the server and in the incremental and document grouping made in web documents.

Clustering is one of the most interesting and important topics in data mining. The purpose of clustering is to find intrinsic structures in the data, and organize them into significant subgroups for further study and analysis. Many clustering algorithms have been published each year.

Existing systems avidly select the next set of frequent elements representing the next group to minimize overlap between documents containing both the set of elements and some sets of remaining elements.

In other words, the result of the grouping depends on the order in which the sets of elements are collected, which in turn depends on the greedy heuristics. This method does not follow a sequential order of clusters selection. Instead, we assign documents to the best cluster.
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