Survey On Distributed Data Mining Video
A Survey on Data, Entity, Event, and Relationship Extraction for Web Mining and Content AnalysisCertainly: Survey On Distributed Data Mining
| The Odyssey By Bernard Evslin | 2 days ago · So, data mining demands the development of tools and algorithms that enable mining of distributed data. Complex Data Real world data is really heterogeneous and it could be multimedia data including images, audio and video, complex data, temporal data, spatial data. 10 hours ago · Read 7 answers by scientists with 18 recommendations from their colleagues to the question asked by Abdullah Al Imran on Aug 22, Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters).It is a main task of exploratory data mining, and a common technique for statistical data . |
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Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group called a cluster are more similar in some sense to each other than to those in other groups clusters. It is a main task of exploratory data mining , and a common technique for statistical data analysis , used in many fields, including pattern recognition , image analysis , information retrieval , bioinformatics , data compression , computer graphics and machine learning. Cluster analysis itself is not one specific algorithm , but the general task to be solved. It can be achieved by various algorithms that differ significantly in their understanding of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small distances between cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter settings including parameters such as the distance function to use, a density threshold or the number of expected clusters depend on the individual data set and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure. It is often necessary to modify data preprocessing and model parameters until the result achieves the desired properties. The subtle differences are often in the use of the results: while in data mining, the resulting groups are the matter of interest, in automatic classification the resulting discriminative power is of interest.
Survey On Distributed Data Mining.
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