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Discrete variables are also called qualitative variables. Such variables are measured, or their values defined, using one of two kinds of nonmetric scales – nominal or ordinal. A nominal scale is an orderless scale, which uses different symbols, characters, and numbers to represent the different states of the variable being measured.

Factor analysis is a model evaluation technique. It is a collection of methods used to examine the underlying constructs’ influence of the responses of variables. It is a model evaluation technique which is a collection of methods used to examine the underlying constructs influence of the response of variables.

The principle component analysis (PCA) aims at representing data using orthogonal vectors so that the original voluminous data can be projected on a smaller space. It differs from attribute selection strategies in that it creates a new and alternative set of variables (attributes) that capture the meaning of database. PCA allows normalization to avoid domination in the process of data compression.

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics.

A multilayer perceptron (MLP) is a feedforward artificial neural network model that maps sets of input data onto a set of appropriate outputs.

Performance of matching between user query and document representation is called Evaluation.

A data compression technique to reduce the redundancies in data representation in order to decrease data storage requirements and hence, communication costs when transmitted through a communication network. Reducing the storage requirement is equivalent to increasing the capacity of the storage medium and hence communication bandwidth.

Data mining is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. It is an interdisciplinary subfield of computer science. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.

A data cube stores data in a summarized version which helps in a faster analysis of data. The data is stored in such a way that it allows reporting easily.

Data Mining as an analytic process designed to explore data (usually large amounts of – typically business or market related – data) in search for consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data.

Discreet data can be considered as defined or finite data. e.g. Mobile numbers. Only finite set of values are available.

Continuous data can be considered as data which changes continuously and in an ordered fashion. e.g. age. Only real numbers are available. E.g. height, weight, length, temperature.

A decision tree is a tool that uses a tree like graph to model decisions and consequences to help managers incorporate uncertainty in valuations. A decision tree is a tree in which every node is either a leaf node or a decision node. A leaf node indicates the value of the target attribute (class) of examples.

Naive Bayes algorithm is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. Naive Bayes model is easy to build and particularly useful for very large data sets. Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods. It is used for estimating the probability of a class value during classification & prediction and generating mining models.

Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values.

Association algorithms find correlations between different attributes in a dataset. The most common application of this kind of algorithm is for creating association rules, which can be used in a market basket analysis.

Data quality is a perception or an assessment of data’s fitness to serve its purpose in a given context. Aspects of data quality include:
– Accuracy
– Completeness
– Update status
– Relevance
– Consistency across data sources
– Reliability
– Appropriate presentation
– Accessibility