Data Warehousing

Data Mining

• exploration,
• model building or pattern definition, and
• validation/verification.

The concept of Data Mining is becoming increasingly popular as a business information management tool where it is expected to reveal knowledge structures that can guide decisions in conditions of limited certainty.  Recently, there has been increased interest in developing new analytic techniques specifically designed to address the issues relevant to business data mining (e.g., Classification Trees).  But, Data Mining is still based on the conceptual principles of traditional Exploratory Data Analysis (EDA) and modeling and it shares with them both general approaches and specific techniques.  

However, an important general difference in the focus and purpose between Data Mining and the traditional Exploratory Data Analysis (EDA) is that Data Mining is more oriented towards applications than the basic nature of the underlying phenomena.  In other words, Data Mining is relatively less concerned with identifying the specific relations between the involved variables.  For example, uncovering the nature of the underlying functions or the specific types of interactive, multivariate dependencies between variables are not the main goal of Data Mining.  Instead, the focus is on producing a solution that can generate useful predictions.  Therefore, Data Mining accepts among others a "black box" approach to data exploration or knowledge discovery and uses not only the traditional Exploratory Data Analysis (EDA) techniques, but also such techniques as Neural Networks which can generate valid predictions but are not capable of identifying the specific nature of the interrelations between the variables on which the predictions are based.  

Data Mining is often considered to be "a blend of statistics, AI [artificial intelligence], and data base research" (Pregibon, 1997, p. 8), which until very recently was not commonly recognized as a field of interest for statisticians, and was even considered by some "a dirty word in Statistics" (Pregibon, 1997, p. 8).  Due to its applied importance, however, the field emerges as a rapidly growing and major area (also in statistics) where important theoretical advances are being made (see, for example, the recent annual International Conferences on Knowledge Discovery and Data Mining, co-hosted in 1997 by the American Statistical Association).  

For information on Data Mining techniques, see Exploratory Data Analysis (EDA) and Data Mining Techniques, see also Neural Networks; for a comprehensive overview and discussion of Data Mining, see Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy (1996).  Representative selections of articles on Data Mining can be found in Proceedings from the American Association of Artificial Intelligence Workshops on Knowledge Discovery in Databases published by AAAI Press (e.g., Piatetsky-Shapiro, 1993; Fayyad & Uthurusamy, 1994).  

Data mining is often treated as the natural extension of the data warehousing concept. 
 

On-Line Analytic Processing (OLAP)

Neural Networks

Neurons apply an iterative process to the number of inputs (variables) to adjust the weights of the network in order to optimally predict (in traditional terms one could say, find a "fit" to) the sample data on which the "training" is performed.  

The resulting "network" developed in the process of "learning" represents a pattern detected in the data.  Thus, in this approach, the "network" is the functional equivalent of a model of relations between variables in the traditional model building approach.  However, unlike in the traditional models  
, in the "network," those relations cannot be articulated in the usual terms used in statistics or methodology to describe relations between variables (such as, for example, "A is positively correlated with B but only for observations where the value of C is low and D is high").  Some neural networks can produce highly accurate predictions; they represent, however, a typical a-theoretical (one can say, "a black box") research approach.  That approach is concerned only with practical considerations, that is, with the predictive validity of the solution and its applied relevance and not with the nature of the underlying mechanism or its relevance for any "theory" of the underlying phenomena.  (However, it should be mentioned that Neural Network techniques can also be used as a component of analyses designed to build explanatory models because Neural Networks can help explore data sets in search for relevant variables or groups of variables; the results of such explorations can then facilitate the process of model building.)  

One of the major advantages of neural networks is that, theoretically, they are capable of approximating any continuous function, and thus the researcher does not need to have any hypotheses about the underlying model, or even to some extent, which variables matter.  An important disadvantage, however, is that the final solution depends on the initial conditions of the network, and, as stated before, it is virtually impossible to "interpret" the solution in traditional, analytic terms, such as those used to build theories that explain phenomena.  
Some authors stress the fact that neural networks use, or one should say, are expected to use, massively parallel computation models.  For example Haykin (1994) defines neural network as:  

"a massively parallel distributed processor that has a natural propensity for storing experiential knowledge and making it available for use.  It resembles the brain in two respects: (1) Knowledge is acquired by the network through a learning process, and (2) Interneuron connection strengths known as synaptic weights are used to store the knowledge." (p. 2).  

However, as Ripley (1996) points out, the vast majority of contemporary neural network applications run on single-processor computers and he argues that a large speed-up can be achieved not only by developing software that will take advantage of multiprocessor hardware by also by designing better (more efficient) learning algorithms.  
Neural networks is one of the methods used in Data Mining; see also Exploratory Data Analysis and Data Mining Techniques.  

For more information on neural networks, see Haykin (1994), Masters (1995), Ripley (1996), and Welstead (1994).  For a discussion of neural networks as statistical tools, see Warner and Misra (1996).