 accuracy

Accuracy is an important factor in assessing the success of data mining. When applied
to data, accuracy refers to the rate of correct values in the data. When applied
to models, accuracy refers to the degree of fit between the model and the data.
This measures how errorfree the model's predictions are. Since accuracy does not
include cost information, it is possible for a less accurate model to be more costeffective.
Also see precision.
 activation function

A function used by a node in a neural net to transform input data from any domain
of values into a finite range of values. The original idea was to approximate the
way neurons fired, and the activation function took on the value 0 until the input
became large and the value jumped to 1. The discontinuity of this 0or1 function
caused mathematical problems, and sigmoidshaped functions (e.g., the logistic function)
are now used.
 antecedent

When an association between two variables is defined, the first item (or lefthand
side) is called the antecedent. For example, in the relationship "When a prospector
buys a pick, he buys a shovel 14% of the time," "buys a pick" is
the antecedent.
 API

An application program interface. When a software system features an API, it provides
a means by which programs written outside of the system can interface with the system
to perform additional functions. For example, a data mining software system may
have an API which permits userwritten programs to perform such tasks as extract
data, perform additional statistical analysis, create specialized charts, generate
a model, or make a prediction from a model.
 associations

An association algorithm creates rules that describe how often events have occurred
together. For example, "When prospectors buy picks, they also buy shovels 14%
of the time." Such relationships are typically expressed with a confidence
interval.


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 backpropagation

A training method used to calculate the weights in a neural net from the data.
 bias

In a neural network, bias refers to the constant terms in the model. (Note that
bias has a different meaning to most data analysts.) Also see precision.
 binning

A data preparation activity that converts continuous data to discrete data by replacing
a value from a continuous range with a bin identifier, where each bin represents
a range of values. For example, age could be converted to bins such as 20 or under,
2140, 4165 and over 65.
 bootstrapping

Training data sets are created by resampling with replacement from the original
training set, so data records may occur more than once. In other words, this method
treats a sample as if it were the entire population. Usually, final estimates are
obtained by taking the average of the estimates from each of the bootstrap test
sets.

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 CART

Classification And Regression Trees. CART is a method of splitting the independent
variables into small groups and fitting a constant function to the small data sets.
In categorical trees, the constant function is one that takes on a finite small
set of values (e.g., Y or N, low or medium or high). In regression trees, the mean
value of the response is fit to small connected data sets.
 categorical data

Categorical data fits into a small number of discrete categories (as opposed to
continuous). Categorical data is either nonordered (nominal) such as gender or
city, or ordered (ordinal) such as high, medium, or low temperatures.
 CHAID

An algorithm for fitting categorical trees. It relies on the chisquared statistic
to split the data into small connected data sets.
 chisquared

A statistic that assesses how well a model fits the data. In data mining, it is
most commonly used to find homogeneous subsets for fitting categorical trees as
in CHAID.
 classification

Refers to the data mining problem of attempting to predict the category of categorical
data by building a model based on some predictor variables.
 classification tree

A decision tree that places categorical variables into classes.
 cleaning (cleansing)

Refers to a step in preparing data for a data mining activity. Obvious data errors
are detected and corrected (e.g., improbable dates) and missing data is replaced.
 clustering

Clustering algorithms find groups of items that are similar. For example, clustering
could be used by an insurance company to group customers according to income, age,
types of policies purchased and prior claims experience. It divides a data set so
that records with similar content are in the same group, and groups are as different
as possible from each other. Since the categories are unspecified, this is sometimes
referred to as unsupervised learning.
 confidence

Confidence of rule "B given A" is a measure of how much more likely it
is that B occurs when A has occurred. It is expressed as a percentage, with 100%
meaning B always occurs if A has occurred. Statisticians refer to this as the conditional
probability of B given A. When used with association rules, the term confidence
is observational rather than predictive. (Statisticians also use this term in an
unrelated way. There are ways to estimate an interval and the probability that the
interval contains the true value of a parameter is called the interval confidence.
So a 95% confidence interval for the mean has a probability of .95 of covering the
true value of the mean.)
 confusion matrix

A confusion matrix shows the counts of the actual versus predicted class values.
It shows not only how well the model predicts, but also presents the details needed
to see exactly where things may have gone wrong.
 consequent

When an association between two variables is defined, the second item (or righthand
side) is called the consequent. For example, in the relationship "When a prospector
buys a pick, he buys a shovel 14% of the time," "buys a shovel" is
the consequent.
 continuous

Continuous data can have any value in an interval of real numbers. That is, the
value does not have to be an integer. Continuous is the opposite of discrete or
categorical.
 cross validation

A method of estimating the accuracy of a classification or regression model. The
data set is divided into several parts, with each part in turn used to test a model
fitted to the remaining parts.


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 data

Values collected through record keeping or by polling, observing, or measuring,
typically organized for analysis or decision making. More simply, data is facts,
transactions and figures.
See Metadata
 data format

Data items can exist in many formats such as text, integer and floatingpoint decimal.
Data format refers to the form of the data in the database.
 data mining

An information extraction activity whose goal is to discover hidden facts contained
in databases. Using a combination of machine learning, statistical analysis, modeling
techniques and database technology, data mining finds patterns and subtle relationships
in data and infers rules that allow the prediction of future results. Typical applications
include market segmentation, customer profiling, fraud detection, evaluation of
retail promotions, and credit risk analysis.
 data mining method

Procedures and algorithms designed to analyze the data in databases.
 DBMS

Database management systems.
 decision tree

A treelike way of representing a collection of hierarchical rules that lead to
a class or value.
 deduction

Deduction infers information that is a logical consequence of the data.
 degree of fit

A measure of how closely the model fits the training data. A common measure is rsquare.
 dependent variable

The dependent variables (outputs or responses) of a model are the variables predicted
by the equation or rules of the model using the independent variables (inputs or
predictors).
 deployment

After the model is trained and validated, it is used to analyze new data and make
predictions. This use of the model is called deployment.
 dimension

Each attribute of a case or occurrence in the data being mined. Stored as a field
in a flat file record or a column of relational database table.
 discrete

A data item that has a finite set of values. Discrete is the opposite of continuous.
 discriminant analysis

A statistical method based on maximum likelihood for determining boundaries that
separate the data into categories.


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 entropy

A way to measure variability other than the variance statistic. Some decision trees
split the data into groups based on minimum entropy.
 exploratory analysis

Looking at data to discover relationships not previously detected. Exploratory analysis
tools typically assist the user in creating tables and graphical displays.
 external data

Data not collected by the organization, such as data available from a reference
book, a government source or a proprietary database.

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 feedforward

A neural net in which the signals only flow in one direction, from the inputs to
the outputs.
 fuzzy logic

Fuzzy logic is applied to fuzzy sets where membership in a fuzzy set is a probability,
not necessarily 0 or 1. Nonfuzzy logic manipulates outcomes that are either true
or false. Fuzzy logic needs to be able to manipulate degrees of "maybe"
in addition to true and false.

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 genetic algorithms

A computerbased method of generating and testing combinations of possible input
parameters to find the optimal output. It uses processes based on natural evolution
concepts such as genetic combination, mutation and natural selection.
 GUI

Graphical User Interface.


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 hidden nodes

The nodes in the hidden layers in a neural net. Unlike input and output nodes, the
number of hidden nodes is not predetermined. The accuracy of the resulting model
is affected by the number of hidden nodes. Since the number of hidden nodes directly
affects the number of parameters in the model, a neural net needs a sufficient number
of hidden nodes to enable it to properly model the underlying behavior. On the other
hand, a net with too many hidden nodes will overfit the data. Some neural net products
include algorithms that search over a number of alternative neural nets by varying
the number of hidden nodes, in the end choosing the model that gets the best results
without overfitting.


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 independent variable

The independent variables (inputs or predictors) of a model are the variables used
in the equation or rules of the model to predict the output (dependent) variable.
 induction

A technique that infers generalizations from the information in the data.
 interaction

Two independent variables interact when changes in the value of one change the effect
on the dependent variable of the other.
 internal data

Data collected by an organization such as operating and customer data.

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 knearest neighbor

A classification method that classifies a point by calculating the distances between
the point and points in the training data set. Then it assigns the point to the
class that is most common among its knearest neighbors (where k is an integer).
 Kohonen feature map

A type of neural network that uses unsupervised learning to find patterns in data.
In data mining it is employed for cluster analysis.


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 layer

Nodes in a neural net are usually grouped into layers, with each layer described
as input, output or hidden. There are as many input nodes as there are input (independent)
variables and as many output nodes as there are output (dependent) variables. Typically,
there are one or two hidden layers.
 leaf

A node not further split  the terminal grouping  in a classification or decision
tree.
 learning

Training models (estimating their parameters) based on existing data.
 least squares

The most common method of training (estimating) the weights (parameters) of a model
by choosing the weights that minimize the sum of the squared deviation of the predicted
values of the model from the observed values of the data.
 lefthand side

When an association between two variables is defined, the first item is called the
lefthand side (or antecedent). For example, in the relationship "When a prospector
buys a pick, he buys a shovel 14% of the time", "buys a pick" is
the lefthand side.
 logistic regression (logistic discriminant analysis)

A generalization of linear regression. It is used for predicting a binary variable
(with values such as yes/no or 0/1). An example of its use is modeling the odds
that a borrower will default on a loan based on the borrower's income, debt and
age.


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 MARS

Multivariate Adaptive Regression Splines. MARS is a generalization of a decision
tree.
 maximum likelihood

Another training or estimation method. The maximum likelihood estimate of a parameter
is the value of a parameter that maximizes the probability that the data came from
the population defined by the parameter.
 mean

The arithmetic average value of a collection of numeric data.
 median

The value in the middle of a collection of ordered data. In other words, the value
with the same number of items above and below it.
 Metadata

Metadata is that data which describes and defines the data within an information
processing environment. The metadata is made available to the programs and people
which need to know the technical and business background of the data in order to
perform the functions of the organization collecting and processing data about its
activities.
Sounds utterly acedemic, doesn't it?
 missing data

Data values can be missing because they were not measured, not answered, were unknown
or were lost. Data mining methods vary in the way they treat missing values. Typically,
they ignore the missing values, or omit any records containing missing values, or
replace missing values with the mode or mean, or infer missing values from existing
values.
 mode

The most common value in a data set. If more than one value occurs the same number
of times, the data is multimodal.
 model

An important function of data mining is the production of a model. A model can be
descriptive or predictive. A descriptive model helps in understanding underlying
processes or behavior. For example, an association model describes consumer behavior.
A predictive model is an equation or set of rules that makes it possible to predict
an unseen or unmeasured value (the dependent variable or output) from other, known
values (independent variables or input). The form of the equation or rules is suggested
by mining data collected from the process under study. Some training or estimation
technique is used to estimate the parameters of the equation or rules.
 MPP

Massively parallel processing, a computer configuration that is able to use hundreds
or thousands of CPUs simultaneously. In MPP each node may be a single CPU or a collection
of SMP CPUs. An MPP collection of SMP nodes is sometimes called an SMP cluster.
Each node has its own copy of the operating system, memory, and disk storage, and
there is a data or process exchange mechanism so that each computer can work on
a different part of a problem. Software must be written specifically to take advantage
of this architecture.

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 neural network

A complex nonlinear modeling technique based on a model of a human neuron. A neural
net is used to predict outputs (dependent variables) from a set of inputs (independent
variables) by taking linear combinations of the inputs and then making nonlinear
transformations of the linear combinations using an activation function. It can
be shown theoretically that such combinations and transformations can approximate
virtually any type of response function. Thus, neural nets use large numbers of
parameters to approximate any model. Neural nets are often applied to predict future
outcome based on prior experience. For example, a neural net application could be
used to predict who will respond to a direct mailing.
 node

A decision point in a classification (i.e., decision) tree. Also, a point in a neural
net that combines input from other nodes and produces an output through application
of an activation function.
 noise

The difference between a model and its predictions. Sometimes data is referred to
as noisy when it contains errors such as many missing or incorrect values or when
there are extraneous columns.
 nonapplicable data

Missing values that would be logically impossible (e.g., pregnant males) or are
obviously not relevant.
 normalize

A collection of numeric data is normalized by subtracting the minimum value from
all values and dividing by the range of the data. This yields data with a similarly
shaped histogram but with all values between 0 and 1. It is useful to do this for
all inputs into neural nets and also for inputs into other regression models. (Also
see standardize.)

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 OLAP

OnLine Analytical Processing tools give the user the capability to perform multidimensional
analysis of the data.
 optimization criterion

A positive function of the difference between predictions and data estimates that
are chosen so as to optimize the function or criterion. Least squares and maximum
likelihood are examples.
 outliers

Technically, outliers are data items that did not (or are thought not to have) come
from the assumed population of data  for example, a nonnumeric when you are expecting
only numeric values. A more casual usage refers to data items that fall outside
the boundaries that enclose most other data items in the data set.
 overfitting

A tendency of some modeling techniques to assign importance to random variations
in the data by declaring them important patterns.
 overlay

Data not collected by the organization, such as data from a proprietary database,
that is combined with the organization's own data.

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 parallel processing

Several computers or CPUs linked together so that each can be computing simultaneously.
 pattern

Analysts and statisticians spend much of their time looking for patterns in data.
A pattern can be a relationship between two variables. Data mining techniques include
automatic pattern discovery that makes it possible to detect complicated nonlinear
relationships in data. Patterns are not the same as causality.
 precision

The precision of an estimate of a parameter in a model is a measure of how variable
the estimate would be over other similar data sets. A very precise estimate would
be one that did not vary much over different data sets. Precision does not measure
accuracy. Accuracy is a measure of how close the estimate is to the real value of
the parameter. Accuracy is measured by the average distance over different data
sets of the estimate from the real value. Estimates can be accurate but not precise,
or precise but not accurate. A precise but inaccurate estimate is usually biased,
with the bias equal to the average distance from the real value of the parameter.
 predictability

Some data mining vendors use predictability of associations or sequences to mean
the same as confidence.
 prevalence

The measure of how often the collection of items in an association occur together
as a percentage of all the transactions. For example, "In 2% of the purchases
at the hardware store, both a pick and a shovel were bought."
 pruning

Eliminating lower level splits or entire subtrees in a decision tree. This term
is also used to describe algorithms that adjust the topology of a neural net by
removing (i.e., pruning) hidden nodes.

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 range

The range of the data is the difference between the maximum value and the minimum
value. Alternatively, range can include the minimum and maximum, as in "The
value ranges from 2 to 8."
 RDBMS

Relational Database Management System.
 regression tree

A decision tree that predicts values of continuous variables.
 resubstitution error

The estimate of error based on the differences between the predicted values of a
trained model and the observed values in the training set.
 righthand side

When an association between two variables is defined, the second item is called
the righthand side (or consequent). For example, in the relationship "When
a prospector buys a pick, he buys a shovel 14% of the time," "buys a shovel"
is the righthand side.
 rsquared

A number between 0 and 1 that measures how well a model fits its training data.
One is a perfect fit; however, zero implies the model has no predictive ability.
It is computed as the covariance between the predicted and observed values divided
by the standard deviations of the predicted and observed values.


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 sampling

Creating a subset of data from the whole. Random sampling attempts to represent
the whole by choosing the sample through a random mechanism.
 sensitivity analysis

Varying the parameters of a model to assess the change in its output.
 sequence discovery

The same as association, except that the time sequence of events is also considered.
For example, "Twenty percent of the people who buy a VCR buy a camcorder within
four months."
 significance

A probability measure of how strongly the data support a certain result (usually
of a statistical test). If the significance of a result is said to be .05, it means
that there is only a .05 probability that the result could have happened by chance
alone. Very low significance (less than .05) is usually taken as evidence that the
data mining model should be accepted since events with very low probability seldom
occur. So if the estimate of a parameter in a model showed a significance of .01
that would be evidence that the parameter must be in the model.
 SMP

Symmetric multiprocessing is a computer configuration where many CPUs share a common
operating system, main memory and disks. They can work on different parts of a problem
at the same time.
 standardize

A collection of numeric data is standardized by subtracting a measure of central
location (such as the mean or median) and by dividing by some measure of spread
(such as the standard deviation, interquartile range or range). This yields data
with a similarly shaped histogram with values centered around 0. It is sometimes
useful to do this with inputs into neural nets and also inputs into other regression
models. (Also see normalize.)
 supervised learning

The collection of techniques where analysis uses a welldefined (known) dependent
variable. All regression and classification techniques are supervised.
 support

The measure of how often the collection of items in an association occur together
as a percentage of all the transactions. For example, "In 2% of the purchases
at the hardware store, both a pick and a shovel were bought."


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 test data

A data set independent of the training data set, used to finetune the estimates
of the model parameters (i.e., weights).
 test error

The estimate of error based on the difference between the predictions of a model
on a test data set and the observed values in the test data set when the test data
set was not used to train the model.
 time series

A series of measurements taken at consecutive points in time. Data mining products
which handle time series incorporate timerelated operators such as moving average.
(Also see windowing.)
 time series model

A model that forecasts future values of a time series based on past values. The
model form and training of the model usually take into consideration the correlation
between values as a function of their separation in time.
 topology

For a neural net, topology refers to the number of layers and the number of nodes
in each layer.
 training

Another term for estimating a model's parameters based on the data set at hand.
 training data

A data set used to estimate or train a model.
 transformation

A reexpression of the data such as aggregating it, normalizing it, changing its
unit of measure, or taking the logarithm of each data item.

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 unsupervised learning

This term refers to the collection of techniques where groupings of the data are
defined without the use of a dependent variable. Cluster analysis is an example.


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 validation

The process of testing the models with a data set different from the training data
set.
 variance

The most commonly used statistical measure of dispersion. The first step is to square
the deviations of a data item from its average value. Then the average of the squared
deviations is calculated to obtain an overall measure of variability.
 visualization

Visualization tools graphically display data to facilitate better understanding
of its meaning. Graphical capabilities range from simple scatter plots to complex
multidimensional representations.

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 windowing

Used when training a model with time series data. A window is the period of time
used for each training case. For example, if we have weekly stock price data that
covers fifty weeks, and we set the window to five weeks, then the first training
case uses weeks one through five and compares its prediction to week six. The second
case uses weeks two through six to predict week seven, and so on.

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