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

Generalized linear models defines the theoretical authority on which linear regression can be used to the modeling of categorical response variables. In generalized linear models, the variance of the response variable, y, is a function of the mean value of y, unlike in linear regression, where the variance of y ... Read More

CBR stands for Case-based reasoning. CBR classifiers need a database of problem solutions to clarify new problems. Unlike nearest-neighbor classifiers, which save training tuples as points in Euclidean space, CBR saves the tuples or “cases” for problem solving as difficult symbolic representation.There are various business applications of CBR include problem ... Read More

Bayesian classifiers are statistical classifiers. They can predict class membership probabilities, including the probability that a given sample belongs to a specific class. Bayesian classifiers have also display large efficiency and speed when it can high databases.Once classes are defined, the system should infer rules that govern the classification, therefore ... Read More

An attribute selection measure is a heuristic for choosing the splitting test that “best” separates a given data partition, D, of class-labeled training tuples into single classes.If it can split D into smaller partitions as per the results of the splitting criterion, ideally every partition can be pure (i.e., some ... Read More