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

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

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

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

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

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

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

There are various methods of clustering which are as follows −Partitioning Methods − Given a database of n objects or data tuples, a partitioning method assembles k partitions of the information, where each partition defines a cluster, and k < n. It can allocate the data into k groups, which ... Read More

There are various applications of clustering which are as follows −Scalability − Some clustering algorithms work well in small data sets including less than 200 data objects; however, a huge database can include millions of objects. Clustering on a sample of a given huge data set can lead to biased ... Read More

There are various challenges of data mining which are as follows −Efficiency and scalability of data mining algorithms − It can effectively extract data from a large amount of data in databases, the knowledge discovery algorithms should be efficient and scalable to huge databases. Specifically, the running time of a ... Read More