GSP stands for Generalised Sequential Patterns. It is a sequential pattern mining method that was produced by Srikant and Agrawal in 1996. It is an expansion of their seminal algorithm for usual itemset mining, referred to as Apriori. GSP needs the downward-closure natures of sequential patterns and adopts a several-pass, ... Read More

Sequential pattern mining is the mining of frequently appearing series events or subsequences as patterns. An instance of a sequential pattern is users who purchase a Canon digital camera are to purchase an HP color printer within a month.For retail information, sequential patterns are beneficial for shelf placement and promotions. ... Read More

STREAM is an individual-pass, constant element approximation algorithm that was produced for the k-medians problem. The k-medians problem is to cluster N data points into k clusters or groups such that the sum squared error (SSQ) between the points and the cluster center to which they are assigned is minimized. ... Read More

Data stream clustering is described as the clustering of data that appar continuously including telephone data, multimedia data, monetary transactions etc. Data stream clustering is generally treated as a streaming algorithm and the objective is, given a sequence of points, to make a best clustering of the stream, utilizing a ... Read More

A user supports two input parameters including the min support threshold, σ, and the error bound previously, indicated as ε. The incoming stream is theoretically divided into buckets of width w = [1/ε].Let N be the current stream length, i.e., the number of items view so far. The algorithm needs ... Read More

Randomized Algorithms − Randomized algorithms in the form of random sampling and blueprint, are used to deal with large, high-dimensional data streams. The need of randomization leads to simpler and more effective algorithms in contrast to known deterministic algorithms.If a randomized algorithm continually returns the correct answer but the running ... Read More

The sequential exception technique simulates the method in which humans can distinguish unusual sets from between a sequence of supposedly like objects. It helps implicit redundancy of the data.Given a data set, D, of n objects, it construct a sequence of subsets, {D1, D2, ..., Dm}, of these objects with ... Read More

A partitioning clustering method is desirable because it minimizes the distance among sets and their cluster centers. If it can choose the k-means method, a cluster center cannot be available given the existence of obstacles.For instance, the cluster can turn out to be in the center of a lake. In ... Read More

PROCLUS stands for Projected Clustering. It is a usual dimension-reduction subspace clustering techniques. That is, rather than starting from individual-dimensional spaces, it begins by finding an original approximation of the clusters in the high-dimensional attribute area.Each dimension is created a weight for each cluster, and the refreshed weights are used ... Read More

CLIQUE was the first algorithm projected for dimension-growth subarea clustering in high-dimensional area. In dimension-growth subarea clustering, the clustering process begins at single-dimensional subspaces and increase upward to higher-dimensional ones.Because CLIQUE partitions each dimension such as grid architecture and decides whether a cell is dense based on the multiple points ... Read More