Data Mining Articles - Page 24 of 36

There are various criteria for selecting the data sources which are as follows −Data accessibility − If two possible feeds exist for the data, one is stored in binary files maintained by a set of programs written before the youngest project team member was born and the other is from a system that reads the binary documents and supports more processing, then the decision is obvious.Data accuracy − As data is passed from system to system, many modifications are made. Sometimes data elements from other systems are added, and sometimes existing elements are processed to create new elements and other ... Read More

There are various tools to facilitate the project are as follows −Data Warehouse Bus Architecture Matrix − The matrix produced by the design team in their internal meetings can be cleaned up and used as a presentation support for meetings with several designers, authority, and end-users. The matrix is very useful as a high-level introduction to the design. It provides each audience a look of what the eventual function of the data warehouse will develop into.Fact Table Diagram − After preparing Bus Architecture the matrix, it can prepare a logical diagram of each completed fact table. The fact table not ... Read More

There are the following methods for designing an Individual Fact Table which is as follows −Choosing the Data Mart − It can be choosing the data mart in the simplest method is the same as choosing the legacy source of information. Typical data marts involve purchase orders, shipments, retail sales, payments, or user connections. These can be an instance of single-source data marts.In some cases, it can define a data mart that should contain multiple-legacy sources. The instance of a multiple-source data mart is user profitability, where legacy sources that define revenue should be combined with legacy sources that represent ... Read More

Dimensional modeling is a logical design method that follows to present the data in a standard structure that is perceptive and enables high-performance access. It is genetically dimensional and observes to a discipline that needs the relational model with several restrictions.Each dimensional model is composed of one table with a multipart key, known as the fact table, and a group of smaller tables known as dimension tables. Each dimension table has an individual element primary key that correlates to one of the elements of the multipart key in the fact table. This distinctive star-like structure is known as star join. ... Read More

There are various approaches of Business Dimensional Lifecycle which are as follows −Project planning − Project planning addresses the description and scoping of the data warehouse project, such as readiness evaluation and business justification. These are the tasks because of the high visibility and costs related to data warehouse projects.Project planning targets resource and skill-level staffing requirements, coupled with project task assignments, continuation, and sequencing. The resulting integrated project plan recognizes all tasks related to the Business Dimensional Lifecycle and the parties included. It can deliver as the foundation for the ongoing administration of the data warehouse project. Project planning ... Read More

Data mining is the procedure of exploration and analysis of huge quantities of data to find meaningful patterns and rules. On the other hand, web mining defines the process of using data mining techniques to extract useful data patterns and trends from web-based records and services, server logs, and hyperlinks. Read this article to learn more about Data Mining and Web Mining and how they are different from each other. What is Data Mining? Data mining is the process of discovering meaningful new correlations, patterns, and trends by shifting through a large amount of data stored in repositories, using pattern ... Read More

In businesses, in order to predict future issues, it is very important to analyze the past and present data. For this purpose, there are several data analysis techniques available like data mining and statistics.Data mining and statistics are used for making data-driven decisions; these are basically the primary components of data science. Data mining and statistics may seem to be similar, but they are quite different from each other. Read this article to learn more about Data Mining and Statistics and how they are different from each other. What is Data Mining? Data miningis the technique of exploration and analysis ... Read More

Big Data represents the vast amount of data that can be structured, semi−structured, and unstructured sets of data ranging in terms of terabytes. In contrast, Data Mining is the process of discovering meaningful new correlations, patterns, and trends by sifting through a large amount of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques. Data mining utilizes tools like machine learning, visualization, statistical models, etc. to extract the useful data from the Big Data. Read this article to find out more about Data Mining and Big Data and how they are different from each ... Read More

Data Mining and Machine Learning are two fields which have influenced each other. Data mining is the field in which operations are performed on sets of data to determine certain patterns in the data sets, whereas machine learning uses certain algorithms that automatically improves the analysis processes through data based experiences. Although data mining and machine learning have many common things, they are quite different from each other. Read this article to learn more about Data Mining and Machine Learning and how they are different from each other. What is Data Mining? Data Mining is the process of discovering ... Read More

The trends in data mining are as follows −Application exploration − Early data mining applications targeted generally on helping businesses gain a competitive edge. The exploration of data mining for businesses continues to expand as e-commerce and e-marketing have become mainstream components of the retail market.Data mining is increasingly used for the exploration of applications in several areas, including financial analysis, telecommunications, biomedicine, and science. Emerging software areas contain data mining for counterterrorism (including and beyond intrusion detection) and mobile (wireless) data mining. As generic data mining systems can have limitations in dealing with application-specific issues, it can view a ... Read More