Glossary

catalog directory

a part of a SAS catalog that stores and maintains information about the name, type, description, and update status of each member of the catalog.

clustering

the process of dividing a data set into mutually exclusive groups so that the observations for each group are as close as possible to one another and different groups are as far as possible from one another. In SAS Text Miner, clustering involves discovering groups of documents that are more similar to each other than they are to the rest of the documents in the collection. When the clusters are determined, examining the words that occur in the cluster reveals the focus of the cluster. Forming clusters within the document collection can help you understand and summarize the collection without reading every document. The clusters can reveal the central themes and key concepts that are emphasized by the collection.

concept linking

finding and displaying the terms that are highly associated with the selected term in the Terms table.

data source

a data object that represents a SAS data set in the Java-based Enterprise Miner GUI. A data source contains all the metadata for a SAS data set that Enterprise Miner needs in order to use the data set in a data mining process flow diagram. The SAS data set metadata that is required to create an SAS Enterprise data source includes the name and location of the data set; the SAS code that is used to define its library path; and the variable roles, measurement levels, and associated attributes that are used in the data mining process.

diagram

See process flow diagram.

entity

any of several types of information that SAS Text Miner is able to distinguish from general text. For example, SAS Text Miner can identify names (of people, places, companies, or products, for example), addresses (including street addresses, post office addresses, e-mail addresses, and URLs), dates, measurements, currency amounts, and many other types of entities.

libref

a name that is temporarily associated with a SAS library. The complete name of a SAS file consists of two words, separated by a period. The libref, which is the first word, indicates the library. The second word is the name of the specific SAS file. For example, in VLIB.NEWBDAY, the libref VLIB tells SAS which library contains the file NEWBDAY. You assign a libref with a LIBNAME statement or with an operating system command.

model

a formula or algorithm that computes outputs from inputs. A data mining model includes information about the conditional distribution of the target variables, given the input variables.

node

(1) in the SAS Enterprise Miner user interface, a graphical object that represents a data mining task in a process flow diagram. The statistical tools that perform the data mining tasks are called nodes when they are placed on a data mining process flow diagram. Each node performs a mathematical or graphical operation as a component of an analytical and predictive data model. (2) in a neural network, a linear or nonlinear computing element that accepts one or more inputs, computes a function of the inputs, and optionally directs the result to one or more other neurons. Nodes are also known as neurons or units. (3) a leaf in a tree diagram. The terms leaf, node, and segment are closely related and sometimes refer to the same part of a tree.

parsing

to analyze text for the purpose of separating it into its constituent words, phrases, multiword terms, punctuation marks, or other types of information.

partitioning

to divide available data into training, validation, and test data sets.

process flow diagram

a graphical representation of the various data mining tasks that are performed by individual Enterprise Miner nodes during a data mining analysis. A process flow diagram consists of two or more individual nodes that are connected in the order in which the data miner wants the corresponding statistical operations to be performed. Short form: PFD.

roll-up terms

the highest-weighted terms in the document collection.

SAS data set

a file whose contents are in one of the native SAS file formats. There are two types of SAS data sets: SAS data files and SAS data views. SAS data files contain data values in addition to descriptor information that is associated with the data. SAS data views contain only the descriptor information plus other information that is required for retrieving data values from other SAS data sets or from files that are stored in other software vendors' file formats.

scoring

the process of applying a model to new data in order to compute output. Scoring is the last process that is performed in data mining.

segmentation

the process of dividing a population into sub-populations of similar individuals. Segmentation can be done in a supervisory mode (using a target variable and various techniques, including decision trees) or without supervision (using clustering or a Kohonen network).

singular value decomposition

a technique through which high-dimensional data is transformed into lower-dimensional data.

source-level debugger

an interactive environment in SAS that enables you to detect and resolve logical errors in programs that are being developed. The debugger consists of windows and a group of commands.

stemming

the process of finding and returning the root form of a word. For example, the root form of grind, grinds, grinding, and ground is grind.

stop list

a SAS data set that contains a simple collection of low-information or extraneous words that you want to remove from text mining analysis.

test data

currently available data that contains input values and target values that are not used during training, but which instead are used for generalization and model comparisons.

training data

currently available data that contains input values and target values that are used for model training.

validation data

data that is used to validate the suitability of a data model that was developed using training data. Both training data sets and validation data sets contain target variable values. Target variable values in the training data are used to train the model. Target variable values in the validation data set are used to compare the training model's predictions to the known target values, assessing the model's fit before using the model to score new data.

variable

a column in a SAS data set or in a SAS data view. The data values for each variable describe a single characteristic for all observations. Each SAS variable can have the following attributes: name, data type (character or numeric), length, format, informat, and label.