The EM (Expectation-Maximization) algorithm is a famous iterative refinement algorithm that can be used for discovering parameter estimates. It can be considered as an extension of the k-means paradigm, which creates an object to the cluster with which it is most similar, depending on the cluster mean.EM creates each object to a cluster according to a weight defining the probability of membership. In other term, there are no strict boundaries among clusters. Thus, new means are evaluated based on weighted measures.EM begins with an original estimate or “guess” of the parameters of the combination model (collectively defined as the parameter ... Read More

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 group of points that map into the cell. This summary data generally fit into the main memory for use by the multiresolution wavelet transform and the subsequent cluster analysis.A wavelet transform is a signal processing approach that decomposes a signal into multiple frequency subbands. The wavelet model can be used ... Read More

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 of the number of data objects, still dependent on only the multiple cells in each dimension in the quantized space.An instance of the grid-based approach involves STING, which explores statistical data stored in the grid cells, WaveCluster, which clusters objects using a wavelet transform approach, and CLIQUE, which defines a ... Read More

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 related design consider cluster proximity yet neglect cluster interconnectivity. In Chameleon, cluster similarity is assessed depending on how well-connected objects are inside a cluster and on the proximity of clusters. Especially, two clusters are combined if their interconnectivity is high and they are close together.It does not base on a ... Read More

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 is chosen in a fairly random manner, it must closely define the original data set. The representative objects (medoids) chosen will be similar to those that would have been selected from the entire data set. CLARA draws several samples of the data set, applies PAM on each sample, and returns ... Read More

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 can lead to partial results. Highly scalable clustering algorithms are required.Ability to deal with different types of attributes − Some algorithms are designed to cluster interval-based (numerical) information. However, applications can require clustering several types of data, including binary, categorical (nominal), and ordinal data, or a combination of these data ... Read More

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 k > 1. For instance, when scanning each transaction in the database to create the frequent 1-itemsets, L1, from the candidate 1-itemsets in C1, it can make some 2-itemsets for each transaction, hash (i.e., map) them into the several buckets of a hash table structure, and increase the equivalent bucket ... Read More

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 product, no user package exists, thus assuring its own desktop client version market. The Web product does not restore administrative and development structures but it restores only user access for queries and updates.Information Advantage Web OLAP − This product uses a server-centric messaging architecture, composed of a powerful analytic engine ... Read More

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 and very few taking more than 20 seconds.Independent research in the Netherlands has shown that end-users consider that a process has declined if results are not received with 30 seconds, and they are suitable to hit ‘ALT+Ctrl+Delete’ unless the system needs them that the report will take longer.Analysis − It ... Read More

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 on the distance among clusters. The broadly used measures for the distance between clusters are as follows, where mi is the mean for cluster Ci, ni is the number of points in Ci, and |p – p’| is the distance among two points p and p'.Types of Hierarchical Clustering MethodsThere ... Read More