Periodicity analysis is the mining of periodic patterns, namely, the search for recurring patterns in time-related series data. Periodicity analysis can be used in several important areas. For example, seasons, tides, planet trajectories, daily power consumptions, daily traffic patterns, and weekly TV programs all present certain periodic patterns.Periodicity analysis is ... Read More

A time-series database includes sequences of values or events accessed over the repeated assessment of time. The values are generally calculated at equal time intervals (e.g., hourly, daily, weekly). Time-series databases are popular in many applications, such as stock market analysis, economic and sales forecasting, budgetary analysis, utility studies, inventory ... Read More

CluStream is an algorithm for the clustering of evolving data streams based on userspecified, online clustering queries. It divides the clustering process into on-line and offline components.The online component computes and stores summary statistics about the data stream using micro-clusters, and performs incremental online computation and maintenance of the micro-clusters. ... Read More

The Hoeffding tree algorithm is a decision tree learning method for stream data classification. It was initially used to track Web clickstreams and construct models to predict which Web hosts and Web sites a user is likely to access. It typically runs in sublinear time and produces a nearly identical ... Read More

BIRCH represents Balanced Iterative Reducing and Clustering Using Hierarchies. It is designed for clustering a huge amount of numerical records by integration of hierarchical clustering and other clustering methods including iterative partitioning.BIRCH offers two concepts, clustering feature and clustering feature tree (CF tree), which are used to summarize cluster description. ... Read More

An object o in a data set S is a distance-based (DB) outlier with parameters p and d, i.e., DB (p, d), if minimum a fraction p of the objects in S lie at a distance higher than d from o. In other words, instead of depending on statistical tests, ... Read More

Conceptual clustering is a form of clustering in machine learning that, given a set of unlabeled objects, makes a classification design over the objects. Unlike conventional clustering, which generally identifies groups of like objects, conceptual clustering goes one step further by also discovering characteristic definitions for each group, where each ... Read More

Semi-supervised clustering is a method that partitions unlabeled data by creating the use of domain knowledge. It is generally expressed as pairwise constraints between instances or just as an additional set of labeled instances.The quality of unsupervised clustering can be essentially improved using some weak structure of supervision, for instance, ... Read More

Constraint-based clustering finds clusters that satisfy user-stated preferences or constraints. It is based on the nature of the constraints, constraint-based clustering can adopt instead of different approaches. There are several categories of constraints which are as follows −Constraints on individual objects − It can define constraints on the objects to ... Read More

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 ... Read More