Ginni has Published 1522 Articles

Why is wavelet transformation useful for clustering?

Ginni

Ginni

Updated on 24-Nov-2021 07:10:54

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WaveCluster is a multiresolution clustering algorithm that first summarizes the records by imposing a multidimensional grid architecture onto the data space. It can use a wavelet transformation to change the original feature space, finding dense domains in the transformed space.In this method, each grid cell summarizes the data of a ... Read More

What is Grid Based Methods?

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Ginni

Updated on 24-Nov-2021 07:08:44

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The grid-based clustering methods use a multi-resolution grid data structure. It quantizes the object areas into a finite number of cells that form a grid structure on which all of the operations for clustering are implemented. The benefit of the method is its quick processing time, which is generally independent ... Read More

What is a Chameleon?

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Ginni

Updated on 24-Nov-2021 07:01:08

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Chameleon is a hierarchical clustering algorithm that uses dynamic modeling to decide the similarity among pairs of clusters. It was changed based on the observed weaknesses of two hierarchical clustering algorithms such as ROCK and CURE.ROCK and related designs emphasize cluster interconnectivity while neglecting data regarding cluster proximity. CURE and ... Read More

How efficient is the k-medoids algorithm on large data sets?

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Ginni

Updated on 24-Nov-2021 06:56:35

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A classic k-medoids partitioning algorithm like PAM works efficiently for small data sets but does not scale well for huge data sets. It can deal with higher data sets, a sampling-based method, known as CLARA (Clustering Large Applications), can be used.The approach behind CLARA is as follows: If the sample ... Read More

What are the requirements of clustering in data mining?

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Ginni

Updated on 24-Nov-2021 06:55:16

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There are the following requirements of clustering in data mining which are as follows −Scalability − Some clustering algorithms work well on small data sets including fewer than some hundred data objects. A huge database can include millions of objects. Clustering on a sample of a given huge data set ... Read More

How can we further improve the efficiency of Apriori-based mining?

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Ginni

Updated on 24-Nov-2021 06:54:02

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There are some variations of the Apriori algorithm that have been projected that target developing the efficiency of the original algorithm which are as follows −The hash-based technique (hashing itemsets into corresponding buckets) − A hash-based technique can be used to decrease the size of the candidate k-itemsets, Ck, for ... Read More

What are the Web-based tools in OLAP?

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Ginni

Updated on 24-Nov-2021 06:46:10

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There are the various web-based tools which are as follows −Arbor Essbase Web − This tool provides features as drilling up, down, across; slice and dice, and powerful reporting, all for OLAP. It also provides data entry, such as full multi-user concurrent write capabilities. Arbor Essbase is only a server ... Read More

What are the characteristics of OLAP?

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Ginni

Updated on 24-Nov-2021 06:44:24

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The FASMI TestIt can represent the characteristics of an OLAP application in a specific method, without dictating how it should be performed.Fast − It defines that the system is targeted to produce most responses to users within about five seconds, with the understandable analysis taking no more than one second ... Read More

What are Hierarchical Methods?

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Ginni

Updated on 24-Nov-2021 06:43:12

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A hierarchical clustering technique works by combining data objects into a tree of clusters. Hierarchical clustering algorithms are either top-down or bottom-up. The quality of an authentic hierarchical clustering method deteriorates from its inability to implement adjustment once a merge or split decision is completed.The merging of clusters is based ... Read More

How does the discordancy testing work?

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Ginni

Updated on 24-Nov-2021 06:38:13

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A statistical discordancy test analysis two hypotheses; a working hypothesis and a different hypothesis. A working hypothesis, H, is a statement that the entire data set of n objects comes from an initial distribution model, F, i.e., H: oi Î F, where i = 1, 2, n.The hypothesis is retained ... Read More

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