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Adaptive Resonance Theory

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An unsupervised learning method created by Stephen Grossberg. ART is a neural network architecture that is aimed at being brain like in unsupervised mode.

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Algorithm

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A step-by-step search in which improvements is made at every step until the best solution is found.

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Apriori Algorithm

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The most commonly used algorithm to discover association rules by recursively identifying frequent item sets.

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Area Under The ROC Curve

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A graphical assessment technique for binary classification models where the true positive rate is plotted on the Y-axis and false positive rate is plotted on the X-axis.

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Artificial Neural Network (ANN)

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Computer technology that attempts to build computers that operate like a human brain. The machines posses simultaneous memory storage and work with ambiguous information. Sometimes called, simply, a neural network. See neural computing.

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Associations

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A category of data mining algorithm that establishes relationships about items that occur together in a given record.

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Axons

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An outgoing connection (i.e., terminal) from a biological neuron.

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Back Propagation

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The best-known learning algorithm in neural computing where the learning is done by comparing computed outputs to desired outputs of training cases.

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Bootstrapping

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A sampling technique where a fixed number of instances for the original data are sampled (with replacements) for training and the rest of the dataset is used for testing.

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Business Analyst

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An individual whose job is to analyze business data. Business analytics involved using DSS tools, especially models, in assisting decision makers. It is essentially OLAP/DSS. See business intelligence (BI).

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Categorical Data

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Data that represents the labels of multiple classes used to divide a variable into specific groups.

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Chromosome

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A candidate solution for a genetic algorithm.

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Classification

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Supervised induction used to analyze the historical data stored in a database and to automatically generate a model that can predict future behaviors.

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Clustering

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Partitioning a databases into segments in which the members of a segment share similar qualities.

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Confidence

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In association rules, the conditional probability of finding the RHS of the rule present in a list of transactions where the LHS of the rule exists.

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Connection Weight

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The weight associated with each link in a neural network model. Neural network learning algorithms assess connection weights.

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CRISP-DM

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A cross-industry standard process of conducting data mining projects, which is a sequence of six steps that starts with a good understanding of the business and the need for the data mining project (i.e., the application domain) and ends with the deployment of the solution that satisfied the specific business need.

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Data Mining

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A process that uses statistical, mathematical, artificial intelligence, and machine-learning techniques to extract and identify useful information and subsequent knowledge from large databases.

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Decision Trees

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A graphical presentation of a sequence of interrelated decisions to be made under assumed risk. This technique classifies specific entities into particular classes based upon the features of the entities into particular classes based upon the features of the entities; a root followed by internal nodes, each node (including root) is labeled with a question, and arcs associated with each node cover all possible responses.

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Dendrites

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The part of a biological neuron that provides inputs to the cell.

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Discovery-driven data mining

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A form of data mining that finds patterns, association, and relationships among data in order to uncover facts that were previously unknown or not even contemplated by an organization.

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Distance Measure

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A method used to calculate the closeness between pairs of items in most cluster analysis methods. Popular distance measures include Euclidian distance (the ordinary distance between two point that one would measure with a ruler) and Manhattan distance (also called the rectilinear distance, or taxicab distance, between two points.)

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Entropy

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A metric that measures the extent of uncertainty or randomness in a data set. In all the data in a subset belong to just one class, then the is no uncertainty or randomness in that data set, and therefore the entropy is zero.

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Fuzzy Logic

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A logically consistent way of reasoning that can cope with uncertain or particle information. Fuzzy logic is characteristic of human thinking and expert systems.

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Genetic Algorithm

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A software program that learns in a evolutionary manner, similar to the way biological systems evolve.

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Gini Index

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A metric that is used in economic to measure the diversity of the population. The same concept can be used to determine the purity of a specific class as a result of a decision to branch along a particular attribute/variable.

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Heuristics

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Informal, judgmental knowledge of a application area that constitutes the rules of good judgement in the field. Heuristics also encompasses the knowledge of how to solve problems efficiently and effectively, how to improve performance, and so on.

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Hidden layer

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The middle layer of an artificial neural network that has three or more layers.

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Hypothesis-dirven data mining

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A form of data mining that begins with a proposition by the user, who then seeks to validate the truthfulness of the proposition.

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Information Gain

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The splitting mechanism used in ID3 (a popular decision-tree algorithm).

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Interval Data

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Variables that can be measured on interval scales.

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K-Fold Cross-Validation

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A popular accuracy assessment technique for prediction models where the complete dataset is randomly split into k mutually exclusive subsets of approximately equal size. The classification model is trained and tested k times. Each time it is trained on all but one fold and then tested on the remaining single fold. The cross-validation estimate of the overall accuracy of a model is calculated by simply averaging the k individual accuracy measures.

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Knowledge Discovery in Databases (KDD)

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A machine-learning process that performs rule induction or a related procedure to establish knowledge from large data bases.

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Kohonen's Self-Organizing Feature Map

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A type of neural network model for machine learning.

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Learning Algorithm

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The training procedure used by an artificial neural network.

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Link Analysis

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The linkage among many objects of interest is discovered automatically, such as the link between Web pages and referential relationships among groups of academic publications authors.

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Machine Learning

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The process by which a computer learns from experience (e.g., using programs that can learn from historical cases)

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Microsoft Enterprise Consortium

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Worldwide source for access to Microsoft's SQL Server 2008 software suite for academic purposes - teaching and research.

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Multi-Layered Perceptron (MLP)

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Layered structure of artificial neural where several hidden layers can be placed between the input and output layers.

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Neural Computing

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An experimental computer design aimed at building intelligent computers that operate in a manner modeled on the functioning of the human brain.

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Neural Network

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See Neural Network (ANN). Computer technology that attempts to build computers that once operate like a human brain. The machine posses simultaneous memory storage and work with ambiguous information. Sometimes called, simply, a neural network.

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Neurons

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A cell (i.e., processing element) of a biological or article neural network.

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Nominal Data

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A type of data that contains measurements of simple codes assigned to objects as labels, which are not measurements. For example, the variable marital status can be generally categorized as (1) single (2) married, and (3) divorced.

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Numeric Data

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A type of data that represents the numeric values of specific variables. Examples of numerically valued variables include age, number of children, total household income (in U.S. dollars), travel distance (in miles), and temperature (in Fahrenheit degrees).

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Ordinal Data

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Data that contains codes assigned to objects or events as labels that also represent that rank order among them. For example, the variable credit score can be generally categorized as (1) low, (2) medium, and (3) high.

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Pattern Recognition

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A technique of matching an external pattern to a pattern stored in a computer's memory (i.e., the process of classifying data into predetermined categories). Pattern recognition is used in inference engines, image processing, neural computing, and speech recognition.

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Prediction

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The act of telling about the future.

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Processing Elements (PE)

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A neuron in a neural network.

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RapidMiner

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A popular, open-source, free-of-charge data mining software suite that employs a graphical enhanced users interface, rather large number of algorithms, and a variety of data visualization features.

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Ratio Data

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Continuous data where both difference and ratios are interpretable. The distinguishing feature of a ratio scale is the possession of a non arbitrary zero value.

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Regression

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A data mining method for real-world prediction problems where the predicted values (i.e., the output variable or dependent variable) are numeric (e.g., predicting he temperature for tomorrow as 68 degree's Fahrenheit.

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Result (Outcome) Variable

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A variable that expresses the result of a decision (e.g., one concerning profit), usually one of the goals of a decision-making problem.

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SAS Enterprise Miner

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A comprehensive, and commercial data mining software tool developed by SAS Institute.

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SEMMA

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An alternative process for data mining projects proposed by the SAS Institute. The acronym "SEMMA" stands for "sample, explore, modify, model, and asses."

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Sensitivity Analysis

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The technique used to detect favorable and unfavorable opinions toward specific products and services using a large numbers of textual data sources (Customer feedback in the form of WEB postings).

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Sequence Mining

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A pattern discovery method where relationships among the things are examined in terms of their order of occurrence to identify associations over time.

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Sigmoid Function

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An S-shaped transfer function in the range of 0 to 1.

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Simple Split

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Data is partitioned into two mutually exclusive subsets called a training set (or holdout set). It is common to designate two-thirds of the data as the training set and the remaining one-thrid as the test set.

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SPSS PASW Modeler

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A very popular, commercially available, comprehensive data, text, and Web mining software suite developed by SPSS (formerly Clementine).

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Summation Function

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A mechanism to add all the inputs coming into a particular neuron.

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Supervised Learning

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A method of training artificial neural networks in which same cases are shown to the network as input, and the weights are adjusted to minimize the error in the outputs.

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Support

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The measure of how often product and/or services appear together in the dataset that contain all of the products and/or services mentioned in a specific rule.

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Support Vector Machines (SVM)

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A family of generalized linear models, which achieve a classification or regression decision based on the value of the linear combination of input features.

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Synapse

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The connection (where the weights are) between processing elements in a neural network.

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Transformation (transfer) Function

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In a neural network, the function that sums and transforms inputs before a neuron fires. It shows the relationship between the internal activation level and the output of a neuron.

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Unsupervised Learning

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A method of training artificial neural networks in which only input stimuli are shown to the network, which is self-organizing.

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Weka

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A popular, free-of-charge, open-source suite of machine-learning software written in Java, developed at the University of Waikato.

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