background preloader

Data Mining: What is Data Mining?

Data Mining: What is Data Mining?
Overview Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. Continuous Innovation Although data mining is a relatively new term, the technology is not. Example For example, one Midwest grocery chain used the data mining capacity of Oracle software to analyze local buying patterns. Data, Information, and Knowledge Data Data are any facts, numbers, or text that can be processed by a computer. Information Knowledge Data Warehouses What can data mining do?

Related:  Data Mining - Text Mining = Fouille de Données, de TextesData mining and other covert collection of Information

Major issues in data mining Every project should be undertaken with all the necessary preparations. Data mining is no different. This tip from... An Introduction to Data Mining An Introduction to Data Mining Discovering hidden value in your data warehouse Overview Text mining A typical application is to scan a set of documents written in a natural language and either model the document set for predictive classification purposes or populate a database or search index with the information extracted. Text mining and text analytics[edit] The term text analytics describes a set of linguistic, statistical, and machine learning techniques that model and structure the information content of textual sources for business intelligence, exploratory data analysis, research, or investigation.[1] The term is roughly synonymous with text mining; indeed, Ronen Feldman modified a 2000 description of "text mining"[2] in 2004 to describe "text analytics

Intelligence Collection and Covert Action: Time for a Divorce? A retired CIA station chief examines they marriage between human intelligence collection and covert action that came about in the early years of the Cold War and its detrimental effects on the Agency’s ability to produce useful and timely intelligence on U.S. enemies. If we cannot eliminate covert action entirely, he concludes, it should at least be separated from the intelligence collection function. – Ed. America has lived with its “Intelligence Community” – the CIA, NSA, DIA and all the other lesser intelligence organizations – for decades. Depending on your viewpoint, they have been somewhere between successful and unsuccessful in providing our government both with the organizational structure and with the intelligence needed to protect our country and advance its international interests. Whatever your take, there is one immutable involved in intelligence work: It is an aggressive, risk-taking business that withers when bureaucratic inertia and caution settle in.

Data Mining and Statistical Modeling A recurring question and point of debate in the realm of analytics is whether there exists any meaningful difference between data mining and statistics. (Text mining or text analytics is not addressed here, although this area of unstructured or semi-structured data analysis has certain similarities as well as points of integration with data mining, the latter dealing with structured data.) Some regard statistics as referring to hypothesis-driven analysis of smaller data sets, while data mining refers to discovery-driven analysis of large databases. Others view the two terms as simply different names for extracting useful information and deriving conclusions from data. Brieman describes two “cultures” or viewpoints about data analysis, with statisticians assuming that observed data are generated by a given data model while data miners make no assumptions about the data generation mechanism and instead rely on algorithms to search for patterns in usually large and complex data sets.

What is Data Mining? A Webopedia Definition Main » TERM » D » By Vangie Beal Data mining requires a class of database applications that look for hidden patterns in a group of data that can be used to predict future behavior. For example, data mining software can help retail companies find customers with common interests. The phrase data mining is commonly misused to describe software that presents data in new ways. Coexistence and Covert Collection An intelligence officer surveys his new opportunities and problems in a coexisting world. George Romano The collection of intelligence information is greatly influenced in its purposes and methods by the state of international affairs; changes in the world situation can create or improve certain opportunities for collection and diminish or even deny others, while shifts in world opinion may seriously affect the advisability of undertaking particular types of intelligence activities. The present time is one of rapid change in world affairs; in general it provides expanding opportunities for collection operations abroad, but at the same time it renders the exposure of these operations by the opposition more damaging than before to our national interest.

A quick introduction to R 'R' is a programming language for data analysis and statistics. It is free, and very widely used by professional statisticians. It is also very popular in certain application areas, including bioinformatics. R is a dynamically typed interpreted language, and is typically used interactively. List of text mining software From Wikipedia, the free encyclopedia Text mining computer programs are available from many commercial and open source companies and sources. Commercial[edit] Commercial and Research[edit] RxNLP API for Text Mining and NLP – text mining APIs for both research and commercial use.