Found 422 Articles for Data Mining

Model-based clustering is a statistical approach to data clustering. The observed (multivariate) data is considered to have been created from a finite combination of component models. Each component model is a probability distribution, generally a parametric multivariate distribution.For instance, in a multivariate Gaussian mixture model, each component is a multivariate Gaussian distribution. The component responsible for generating a particular observation determines the cluster to which the observation belongs.Model-based clustering is a try to advance the fit between the given data and some mathematical model and is based on the assumption that data are created by a combination of a basic ... Read More

The grid-based clustering methods use a multi-resolution grid data structure. It quantizes the object areas into a finite number of cells that form a grid structure on which all of the operations for clustering are implemented. The benefit of the method is its quick processing time, which is generally independent of the number of data objects, still dependent on only the multiple cells in each dimension in the quantized space.The grid-based clustering uses a multi-resolution grid data structure and uses dense grid cells to form clusters. There are several interesting methods are STING, wave cluster, and CLIQUE.STING − A statistical ... Read More

Unsupervised learning is when it can provide a set of unlabelled data, which it is required to analyze and find patterns inside. The examples are dimension reduction and clustering. The training is supported to the machine with the group of data that has not been labeled, classified, or categorized, and the algorithm required to facilitate on that data without some supervision. The objective of unsupervised learning is to restructure the input record into new features or a set of objects with same patterns.Cluster analysis is used to form groups or clusters of the same records depending on various measures made ... Read More

There are two types of partitional algorithms which are as follows −K-means clustering − K-means clustering is the most common partitioning algorithm. K-means reassigns each data in the dataset to only one of the new clusters formed. A record or data point is assigned to the nearest cluster using a measure of distance or similarity. There are the following steps used in the K-means clustering:It can select K initial cluster centroid c1, c2, c3 ... . ck.It can assign each instance x in the S cluster whose centroid is nearest to x.For each cluster, recompute its centroid based on which ... Read More

Relational database systems supports five built-in aggregate functions such as count(), sum(), avg(), max() and min(). These aggregate functions can be used as basic measures in the descriptive mining of multidimensional information. There are two descriptive statistical measures such as measures of central tendency and measures of data dispersion can be used effectively in high multidimensional databases.Measures of central tendency − Measures of central tendency such as mean, median, mode, and mid-range.Mean − The arithmetic average is evaluated simply by inserting together all values and splitting them by the number of values. It uses data from every single value. Let ... Read More

It is a statistical approach for preprocessing data to filter out irrelevant attributes or rank the relevant attribute. Measures of attribute relevance analysis can be used to recognize irrelevant attributes that can be unauthorized from the concept description process. The incorporation of this preprocessing step into class characterization or comparison is defined as an analytical characterization.Data discrimination makes discrimination rules which are a comparison of the general features of objects between two classes defined as the target class and the contrasting class.It is a comparison of the general characteristics of targeting class data objects with the general characteristics of objects ... Read More

Class discrimination is defined as classism. It is prejudice or discrimination based on social class. It involves individual attitudes, behaviors, systems of policies, and practices that are set up to benefit the upper class at the amount as the lower class.Classism can define personal prejudice against lower classes and institutional classism, just as the term racism can define either strictly to personal prejudice or institutional racism. The latter has been represented as how conscious or unconscious classism is clear in the several institutions of our society".Class discrimination can be viewed in several forms of media including television shows, films, and ... Read More

Data generalization summarizes data by replacing relatively low-level values (including numeric value for attribute age) with high-level concepts (including young, middle-aged, and senior). Therefore, it is a process that abstracts a huge set of task-relevant information in a database from a relatively low conceptual level to higher conceptual levels.Following are the two approaches for the efficient and flexible generalization of large data sets −OLAP approach − The data cube technology can be treated as a data warehouse-based, pre-computation-oriented, materialized view approach. It implements offline aggregation earlier an OLAP or data mining query is moved for processing.Attribute-oriented induction approach − It ... Read More

Agglomerative clustering is a bottom-up clustering method where clusters have subclusters, which in turn have sub-clusters, etc. It can start by placing each object in its cluster and then mix these atomic clusters into higher and higher clusters until all the objects are in an individual cluster or until it needs definite termination condition. Some hierarchical clustering methods used to this type. The distinct only in their description of between-cluster similarity.For example, a method called AGNES (Agglomerative Nesting), need the single-link techniques and operates as follows. Consider there are group of objects placed in a rectangle. Initially, every object is ... Read More

Association rule learning is a type of unsupervised learning methods that tests for the dependence of one data element on another data element and create appropriately so that it can be more effective. It tries to discover some interesting relations or relations among the variables of the dataset. It depends on several rules to find interesting relations between variables in the database.The association rule learning is the important technique of machine learning, and it is employed in Market Basket analysis, Web usage mining, continuous production, etc. In market basket analysis, it is adequate used by several big retailers to find ... Read More