Found 427 Articles for Data Mining

A neural network is an array of algorithms that endeavors to identify fundamental relationships in a set of data through a process that mimics the techniques the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial.Neural networks are applicable in virtually every situation in which a relationship between the predictor variables (independents, inputs) and predicted variables (dependents, outputs) exists, even when that relationship is very complex and not easy to articulate in the usual terms of “correlations” or “differences between groups.”There are various applications of Neural Networks which are as follows −Detection ... Read More

A neural network is a series of algorithms that endeavors to recognize basic relationships in a set of record through a process that mimics the way the human brain operates. In this method, neural networks defines systems of neurons, either organic or artificial.Neural Networks are analytic techniques modeled after the (hypothesized) processes of learning in the cognitive system and the neurological functions of the brain and capable of predicting new observations (on specific variables) from other observations after implementing a process of so-called learning from existing information. Neural Networks are one of the Data Mining techniques.The first phase is to ... Read More

Similarity measures provide the framework on which some data mining decisions are based. Tasks including classification and clustering generally consider the existence of some similarity measure, while fields with poor techniques to evaluate similarity often find that searching information is a cumbersome function.There are several applications of similarity measures are as follows −Information Retrieval − The goal of information retrieval (IR) systems is to meet user’s needs. In another terms, a need is generally manifested in the form of a short textual query introduced in the text box of some search engine online. IR systems generally do not directly answer ... Read More

Advantages of Data MiningThe advantage of data mining are as follows −Marketing/RetailingData mining can help direct marketers by supporting them with useful and accurate trends about their users purchasing behavior. It is based on these trends, marketers can direct their marketing attention to their customers with more precision. For example, marketers of a software company may advertise their new software to consumers who have a lot of software purchasing history.Moreover, data mining can also help marketers in predicting which products their users can be interested in purchasing. Through this prediction, marketers can surprise their users and create the users shopping ... Read More

Data mining is the process of discovering meaningful new correlations, patterns, and trends by shifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques. It is the analysis of observational datasets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner.It is the procedure of selection, exploration, and modeling of high quantities of information to find regularities or relations that are at first unknown to obtain clear and beneficial results for the owner of the database. Data mining ... Read More

An OLAM server performs analytical mining in data cubes similarly to an OLAP server performs online analytical processing. An integrated OLAM and OLAP mechanism, where the OLAM and OLAP servers both accept user online queries (or commands) via a graphical user interface API and operate with the data cube in the data analysis via a cube API.A metadata directory can be used to instruct the access of the data cube. The data cube can be created by accessing and integrating multiple databases via an MDDB API and by filtering a data warehouse via a database API that can provide OLE ... Read More

Relational OLAP (ROLAP) serversThese are the intermediate servers that stand in among a relational back-end server and client front-end tools. They facilitate a relational or extended-relational DBMS to save and manage warehouse data, and OLAP middleware to provide the missing item.ROLAP servers involve optimization for each DBMS back end, implementation of aggregation navigation logic, and more tools and services. ROLAP technology tends to have higher scalability than MOLAP technology. The DSS server of Micro strategy, for instance, adopts the ROLAP techniques.ROLAP systems work generally from the data that resides in a relational database, where the base data and dimension tables ... Read More

OLAM stands for Online analytical mining. It is also known as OLAP Mining. It integrates online analytical processing with data mining and mining knowledge in multi-dimensional databases. There are several paradigms and structures of data mining systems.Various data mining tools must work on integrated, consistent, and cleaned data. This requires costly pre-processing for data cleaning, data transformation, and data integration. Thus, a data warehouse constructed by such pre-processing is a valuable source of high-quality information for both OLAP and data mining. Data mining can serve as a valuable tool for data cleaning and data integration.OLAM is particularly important for the ... Read More

Data warehouses and data marts are used in a broad area of applications. Business executives use the data in data warehouses and data marts to implement data analysis and create strategic decisions. In some firms, data warehouses are used as an integral element of a plan-execute-assess “closed-loop” feedback system for enterprise administration.Data warehouses are used widely in banking and financial services, consumer goods and retail distribution sectors, and controlled manufacturing, including demand-based production. Generally, the longer a data warehouse has been in use, the more it will have developed. This evolution takes place throughout various phases.Initially, the data warehouse is ... Read More

Data warehouses contain huge volumes of data. OLAP servers demand that decision support queries be acknowledged in the order of seconds. Thus, it is essential for data warehouse systems to provide highly effective cube computation techniques, access techniques, and query processing techniques.Efficient Computation of Data CubesAt the core of multidimensional data analysis is the efficient computation of aggregations across many sets of dimensions. In SQL terms, these aggregations are referred to as group-by’s. Each group-by can be represented by a cuboid, where the set of group-by’s forms a lattice of cuboids defining a data cube.There are three choices for data ... Read More