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Data Structure Articles
Page 117 of 164
What is Sequential Exception Technique?
The sequential exception technique simulates the method in which humans can distinguish unusual sets from between a sequence of supposedly like objects. It helps implicit redundancy of the data.Given a data set, D, of n objects, it construct a sequence of subsets, {D1, D2, ..., Dm}, of these objects with 2 ≤ m ≤ n including$$\mathrm{D_{j−1}\subset D_{j}\:\:where\: D_{j}\subseteq D}$$Dissimilarities are assessed between subsets in the series. The technique learns the following terms which are as follows −Exception set − This is the set of deviations or outliers. It is defined as the smallest subset of objects whose removal results in ...
Read MoreHow can we approach the problem of clustering with obstacles?
A partitioning clustering method is desirable because it minimizes the distance among sets and their cluster centers. If it can choose the k-means method, a cluster center cannot be available given the existence of obstacles.For instance, the cluster can turn out to be in the center of a lake. In other words, the k-medoids method chooses an object inside the cluster as a center and thus guarantees that a problem cannot appear.At each time a new medoid is selected, the distance among each object and its newly selected cluster center has to be recalculated. Because there can be obstacles among ...
Read MoreWhat is PROCLUS?
PROCLUS stands for Projected Clustering. It is a usual dimension-reduction subspace clustering techniques. That is, rather than starting from individual-dimensional spaces, it begins by finding an original approximation of the clusters in the high-dimensional attribute area.Each dimension is created a weight for each cluster, and the refreshed weights are used in the next iteration to recreate the clusters. This leads to the exploration of dense areas in all subspaces of some convenient dimensionality and prevents the generation of a huge number of overlapped clusters in projected dimensions of lower dimensionality.PROCLUS discover the best group of medoids by a hill-climbing phase ...
Read MoreWhat is CLIQUE?
CLIQUE was the first algorithm projected for dimension-growth subarea clustering in high-dimensional area. In dimension-growth subarea clustering, the clustering process begins at single-dimensional subspaces and increase upward to higher-dimensional ones.Because CLIQUE partitions each dimension such as grid architecture and decides whether a cell is dense based on the multiple points it includes. It can be looked as an integration of density-based and grid-based clustering approaches.The ideas of the CLIQUE clustering algorithm are as follows −Given a large group of multidimensional data points, the data area is generally not uniformly engaged by the data points. CLIQUE’s clustering recognizes the sparse and ...
Read MoreWhat is the working of COWEB?
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 MoreHow is this statistical information useful for query answering?
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 MoreWhat is STING?
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 MoreWhat is DENCLUE?
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 MoreWhat is DBSCAN?
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 MoreWhat is ROCK?
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 ...
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