Found 427 Articles for Data Mining

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 of the resulting division. The placement that results in the highest element utility must be a best host for the object.COBWEB also calculates the category utility of the partition that can result if a new node is made for the object. The object is located in a current class, or ... 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 interval (or estimated range of probability) reflecting the cell’s relevancy to the given query.The statistical parameters of higher-level cells can simply be calculated from the parameters of the lower-level cells. These parameters contain the following − the attribute-independent parameter, count, and the attribute-dependent parameters, mean, stdev (standard deviation), min (minimum), ... 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 is separation to form several cells at the next lower level.Statistical data regarding the attributes in each grid cell (including the mean, maximum, and minimum values) is precomputed and stored. Statistical parameters of higher-level cells can simply be calculated from the parameters of the lower-level cells.These parameters contain the following ... 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 cluster model depends on kernel density estimation. A cluster is represented by a local maximum of the predicted density function.DENCLUE doesn't operate on records with uniform distribution. In high dimensional space, the data always look like uniformly distributed because of the curse of dimensionality. Hence, DENCLUDE doesn't operate well on ... 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 of density-based clustering includes a number of new definitions as follows −The neighborhood within a radius ε of a given object is known as the εneighborhood of the object.If the ε-neighborhood of an object includes at least a minimum number, MinPts, of objects, then the object is known as core ... 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 create only the similarity among points when clustering i.e., at each step, points that are combined into a single cluster. This “localized” method is prone to bugs. For instance, two distinct clusters can have a few points or outliers that are near; thus, relying on the similarity among points to ... 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 as the cluster’s centroid or center of gravity.The k-means algorithm proceeds as follows. First, it can randomly choose k of the objects, each of which originally defines a cluster mean or center. For each of the remaining objects, an object is created to the cluster to which it is the ... 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. It can be considering binary variables as if they are interval-scaled can lead to misleading clustering outcomes. Hence, methods defines to binary data are essential for calculating dissimilarities.There is one method involves calculating a dissimilarity matrix from the given binary data. If some binary variables are thought of as having ... 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 to pounds for weight, can lead to several clustering structure. In general, defining a variable in smaller units will lead to a higher range for that variable, and therefore a larger effect on the resulting clustering architecture.It can prevent dependence on the choice of data units, the data must be ... 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 (proportion of positive tuples that are recognized) and the false-positive rate (proportion of negative tuples that are incorrectly recognized as positive) for a given model.Given a two-class problem, it enables us to anticipate the trade-off between the rate at which the model can accurately identify ‘yes’ cases versus the rate ... Read More