Found 427 Articles for Data Mining

Data Warehousing is an approach that can collect and manage data from multiple sources to provide the business a significant business insight. A data warehouse is specifically designed to provide management decisions.In simple terms, a data warehouse defines a database that is maintained independently from an organization’s operational databases. Data warehouse systems enable the integration of multiple application systems. They support data processing by providing a solid platform of consolidated, historical data for analysis.A data warehouse is a semantically consistent data save that handle as a physical execution of a decision support data model. It saves the data an enterprise ... Read More

There are various methods for the generation of concept hierarchies for nominal data as follows −Specification of a partial ordering of attributes explicitly at the schema level by users or professionals − Concept hierarchies for nominal attributes or dimensions generally contains a set of attributes. A user or professionals can simply represent a concept hierarchy by defining a partial or total governing of the attributes at the schema level.For instance, suppose that a relational database includes the following set of attributes such as street, city, province or state, and country. A data warehouse location dimension can include the same attributes. ... Read More

There are the major steps involved in data preprocessing, namely, data cleaning, data integration, data reduction, and data transformation as follows −Data Cleaning − Data cleaning routines operate to “clean” the information by filling in missing values, smoothing noisy information, identifying or eliminating outliers, and resolving deviation. If users understand the data are dirty, they are unlikely to trust the results of some data mining that has been used.Moreover, dirty data can make confusion for the mining phase, resulting in unstable output. Some mining routines have some phase for dealing with incomplete or noisy information, they are not always potent. ... Read More

An attribute is a data field, defining a characteristic of a data object. The nouns attribute, dimension, feature, and variable are used correspondently in the literature. The dimension is generally used in data warehousing. Machine learning literature influence to use the term feature, while statisticians prefer the method svariable.Data mining and database experts generally use the term attribute. Attributes defining a user object can include, for instance, customer ID, name, and address. Observed values for a given attribute are referred to as observations.A set of attributes can define a given object is known as attribute vector (or feature vector). The ... Read More

There are various aspects of mining methodology which are as follows −Mining various and new kinds of knowledge − Data mining covers a broad spectrum of data analysis and knowledge discovery services, from data characterization and discrimination to relations and correlation analysis, classification, regression, clustering, outlier methods, sequence methods, and trend and computational analysis.These services can use the same database in multiple ways and need the development of several data mining techniques. Because of the diversity of software, new mining services continue to emerge, developing data mining a powerful and fast-increasing field.For instance, for effective knowledge discovery in data networks, ... Read More

A web search engine is a specialized computer server that searches for data on the Web. The search results of a user query are restored as a list (known as hits). The hits can include web pages, images, and different types of files.There are various search engines also search and return data available in public databases or open directories. Search engines differ from web directories in that web directories are supported by human editors whereas search engines works algorithmically or by a combination of algorithmic and human input.Web search engines are large data mining applications. There are several data mining ... Read More

Classification is the procedure of discovering a model that defines and categorize data classes or concepts. The model are based on the search of a set of training data (i.e., data objects for which the class labels are famous). The model can predict the class label of objects for which the class label is anonymous.The derived model can be represented in several forms, including classification rules (i.e., IF-THEN rules), decision trees, numerical formulae, or neural networks. A decision tree is a flowchart-like tree architecture, where each node indicates a test on an attribute value, each branch defines an outcome of ... Read More

One solution is for such companies to support consumers with several opt-out choices, enabling consumers to describe limitations on the use of their personal information, such asThe consumer’s personal data are not to be utilized at all for data mining.The consumer’s data can be used for data mining, but the identity of each consumer or some data that can lead to the disclosure of a person’s identity should be deleted.The data can be used for in-house mining only.The data can be used in-house and externally as well.Furthermore, companies can support consumers with positive consent, i.e., by enabling consumers to opt ... Read More

Visual data mining finds implicit and beneficial knowledge from huge data sets using data and knowledge visualization methods. The human visual system is managed by the eyes and brain, the latter of which can be think of as a dynamic, largely parallel processing and reasoning engine including a huge knowledge base.Visual data mining can be considered as a unification of two disciplines such as data visualization and data mining. It can also associated with computer graphics, multimedia systems, human computer interaction, pattern identification, and highperformance computing.In general, data visualization and data mining can be integrated in the following ways −Data ... Read More

There are several theories for the basis of data mining include the following −Data reduction − In this theory, the basis of data mining is to reduce the data representation. Data reduction trades certainty for speed in response to the need to obtain fast approximate answers to queries on huge databases.Data reduction methods include singular value decomposition (the driving component behind principal components analysis), wavelets, regression, log-linear models, histograms, clustering, sampling, and the development of index trees.Data compression − According to this theory, the basis of data mining is to compress the given information by encoding in terms of bits, ... Read More