COBWEB incrementally include objects into a classification tree. COBWEB descends the tree along an allocate path, refreshing counts along the method, in search of the “best host” or node at which to define the object.This decision depends on temporarily locating the object in each node and calculating the category utility ... Read More

The statistical parameters can be used in a top-down, grid-based approaches as follows. First, a layer within the hierarchical architecture is decided from which the query-answering procedure is to start.This layer generally includes a small number of cells. For every cell in the current layer, it can compute the confidence ... Read More

STING stands for Statistical Information Grid. STING is a grid-based multiresolution clustering method in which the spatial area is divided into rectangular cells. There are several methods of such rectangular cells equivalent to multiple methods of resolution, and these cells form a hierarchical structure each cell at a high level ... Read More

Clustering is the significant data mining approaches for knowledge discovery. The clustering is an exploratory data analysis methods that categorizes several data objects into same groups, such as clusters.DENCLUE represents Density-based Clustering. It is a clustering approach depends on a group of density distribution functions. The DENCLUE algorithm use a ... Read More

DBSCAN stands for Density-Based Spatial Clustering of Applications with Noise. It is a density based clustering algorithm. The algorithm increase regions with sufficiently high density into clusters and finds clusters of arbitrary architecture in spatial databases with noise. It represents a cluster as a maximum group of density-connected points.The concept ... Read More

ROCK stands for Robust Clustering using links. It is a hierarchical clustering algorithm that analyze the concept of links (the number of common neighbours among two objects) for data with categorical attributes. It display that such distance data cannot lead to high-quality clusters when clustering categorical information.Moreover, most clustering algorithms ... Read More

The k-means algorithm creates the input parameter, k, and division a group of n objects into k clusters so that the resulting intracluster similarity is large but the intercluster analogy is low. Cluster similarity is computed regarding the mean value of the objects in a cluster, which can be looked ... Read More

A binary variable has only two states such as 0 or 1, where 0 defines that the variable is absent, and 1 defines that it is present. Given the variable smoker defining a patient, for example, 1 denotes that the patient smokes, while 0 denotes that the patient does not. ... Read More

Interval-scaled variables are continuous data of an approximately linear scale. An examples such as weight and height, latitude and longitude coordinates (e.g., when clustering homes), and weather temperature. The measurement unit used can influence the clustering analysis.For instance, changing data units from meters to inches for height, or from kilograms ... Read More

ROC stands for Receiver Operating Characteristic. ROC curves are a convenient visual tool for analyzing two classification models. ROC curves appears from signal detection theory that was produced during World War II for the search of radar images.An ROC curve displays the trade-off among the true positive rate or sensitivity ... Read More