Random Set

Related Definitions

Random variable: It is not a variable in the traditional sense of the world.  It is actually a function. The outcome of an experiment need not be a number, for example, the outcome when a coin is tossed can be 'heads' or 'tails'. However, we often want to represent outcomes as numbers [STEPS]. A random variable is a function that associates a unique numerical value with every outcome of an experiment. Random variable is written as capital letter, usually X.

Probability distribution and probability density are similar, where distribution is used for discrete  variable and density is used for continuous variable.

Expected Value: The expected value (or population mean) of a random variable indicates its average or central value. It is a useful summary value (a number) of the variable's distribution [STEPS].

The expected value of a random variable X is symbolised by E(X) or µ.

If X is a discrete random variable with possible values x1, x2, x3, ..., xn, and p(xi) denotes P(X = xi), then the expected value of X is defined by:

where the elements are summed over all values of the random variable X.

Prestigious Rankers of Journals and Conferences

Institute for Scientific Information (ISI)
http://en.wikipedia.org/wiki/Institute_for_Scientific_Information

Data Mining

Introduction: Knowledge Discovery and Data Mining (KDD) is an interdisciplinary area focusing upon methodologies for extracting useful knowledge from data. The ongoing rapid growth of online data due to the Internet and the widespread use of databases have created an immense need for KDD methodologies. The challenge of extracting knowledge from data draws upon research in statistics, databases, pattern recognition, machine learning, data visualization, optimization, and high-performance computing, to deliver advanced business intelligence and web discovery solutions [IBM Research].

"The basic task of KDD is to extract knowledge (or information) from lower level data (databases)." [1] Data, in its raw form, is simply a collection of elements, from which little knowledge can be gleaned. With the development of data discovery techniques the value of the data is significantly improved [UF].

A variety of methods are available to assist in extracting patterns that when interpreted provide valuable, possibly previously unknown, insight into the stored data. This information can be predictive or descriptive in nature. Data mining, the pattern extraction phase of KDD, can take on many forms, the choice dependent on the desired results. KDD is a multi-step process that facilitates the conversion of data to useful information.