The discovery of frequent substructures usually consists of two steps. In the first step, it can make frequent substructure candidates. The frequency of every candidate is tested in the second step. Most studies on frequent substructure discovery focus on the optimization of the first step because the second step involves a subgraph isomorphism test whose computational complexity is excessively high (i.e., NP-complete).There are various methods for frequent substructure mining which are as follows −Apriori-based Approach − Apriori-based frequent substructure mining algorithms send the same features with Apriori-based frequent itemset mining algorithms. The search for frequent graphs begins with graphs of ... Read More

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 implemented over time-series data, which includes sequences of values or events generally measured at equal time intervals (e.g., hourly, daily, weekly). It can also be applied to other time-related sequence data where the value or event may occur at a non-equal time interval or at any time (e.g., online transactions). ... 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 studies, yield projections, workload projections, process and quality control, observation of natural phenomena (including atmosphere, temperature, wind, and earthquake), numerical and engineering experiments, and medical treatments.A time-series database is also a sequence database. A sequence database is any database that includes sequences of ordered events, with or without a concrete ... 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. The offline component does macro-clustering and answers various user questions using the stored summary statistics, which are based on the tilted time frame model.The cluster evolving data streams based on both historical and current stream data information, the tilted time frame model (such as a progressive logarithmic model) is adopted, ... 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 decision tree to that of traditional batch learners.It uses Hoeffding trees, which exploit the idea that a small sample can often be enough to choose an optimal splitting attribute. This idea is supported mathematically by the Hoeffding bound (or additive Chernoff bound).Suppose we make N independent observations of a random ... 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. These structures facilitate the clustering method to achieve the best speed and scalability in huge databases and also create it effective for incremental and dynamic clustering of incoming objects.Given n d-dimensional data objects or points in a cluster, and it can represent the centroid x0, radius R, and diameter D ... 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, it can think of distance-based outliers as those objects who do not have enough neighbors.The neighbors are represented based on distance from the given object. In comparison with statistical-based methods, distance-based outlier detection generalizes or merges the ideas behind discordancy testing for standard distributions. Hence, a distance-based outlier is also ... 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, in the form of pairwise constraints (i.e., pairs of objects labeled as belonging to similar or different clusters). Such a clustering procedure that depends on user feedback or guidance constraints is known as semisupervised clustering.There are several methods for semi-supervised clustering that can be divided into two classes which are ... 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 be clustered. In a real estate application, for instance, one can like to spatially cluster only those luxury mansions worth over a million dollars. This constraint confines the collection of objects to be clustered. It can simply be managed by preprocessing (e.g., implementing selection using an SQL query), after which ... 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 group defines a concept or class.Therefore, conceptual clustering is a two-step process − clustering is implemented first, followed by characterization. Thus, clustering quality is not solely a service of single objects. Most techniques of conceptual clustering adopt a statistical method that uses probability measurements in deciding the concepts or clusters.Probabilistic ... Read More