There are various techniques are required to handle specific constraints. The general principles of handling hard and soft constraints which are as follows −Handling Hard Constraints − A general methods for handling difficult constraints is to strictly regard the constraints in the cluster assignment procedure. Given a data set and ... Read More

There are two types of measures such as geodesic distance and distance based on random walk.Geodesic Distance − A simple measure of the distance among two vertices in a graph is the shortest route among the vertices. Usually, the geodesic distance among two vertices is the length in terms of ... Read More

Constraint-based algorithms need constraints to decrease the search area in the frequent itemset generation phase (the association rule creating step is exact to that of exhaustive algorithms).The importance of constraints is well-defined and they make only association rules that are interesting to customers. The method is quite trivial and the ... Read More

In a bipartite graph, vertices can be splitted into two disjoint sets so that each edge connected a vertex in one set to a vertex in the multiple set. For the AllElectronics user purchase data, one set of vertices defines users, with one users per vertex. The multiple set defines ... Read More

There are several methods have been categorized into three major groups including subspace search techniques, correlation-based clustering techniques, and biclustering techniques.Subspace Search Technique − A subspace search method searches several subspaces for clusters. Therefore, a cluster is a subset of objects that are the same as each other in a ... Read More

Active learning is a repetitive type of supervised learning that is relevant for situations where data are sufficient, but the class labels are scarce or costly to acquire. The learning algorithm is active in that it can carefully query a user (e.g., a person oracle) for labels. The multiple tuples ... Read More

The naıve Bayesian classifier makes the assumption of class conditional independence, i.e., given the class label of a tuple, the values of the attributes are assumed to be conditionally independent of one another. This simplifies computation.When the assumption influence true, therefore the naïve Bayesian classifier is the efficient in comparison ... Read More

Perception-based classification (PBC) is an interactive method based on multidimensional visualization methods and enable the user to incorporate background knowledge about the data when constructing a decision tree.By optically interacting with the data, the user is likely to produce a deeper learning of the data. The resulting trees likely to ... Read More

There are various applications of Pattern Mining which are as follows −Pattern mining is generally used for noise filtering and data cleaning as preprocessing in several data-intensive applications. It can be used to explore microarray data, for example, which includes tens of thousands of dimensions (e.g., describing genes).Pattern mining provides ... Read More

The following are general optimization techniques for efficient computation of data cubes which as follows −Sorting, hashing, and grouping − Sorting, hashing, and grouping operations must be used to the dimension attributes to reorder and cluster associated tuples. In cube computation, aggregation is implemented on the tuples that share the ... Read More