#discriminant analysis

10 Types of Data Analysis

(Mathematical and Statistical Methods) 1. Descriptive Analysis: It does what the name suggests - "Describe." It looks at data and analyzes past events for deciding how to approach the future.

2.  Regression Analysis: It allows modeling the relationship between a dependent variable and one or more independent variables.

3.  Dispersion Analysis: Dispersion is the spread to which a set of data is stretched. It is a technique of describing how extended a set of data is.

4.  Factor Analysis: Factor analysis is a regression based data analysis technique, used to find an underlying structure in a set of variables.

5.  Discriminant Analysis: The discriminant analysis utilizes variable measurements on different groups of items to underline points that distinguish the groups.

6.  Time Series: It is a process of modeling and explaining time-dependent series of data points. The goal is to draw meaningful information (rules, patterns) from the shape of data.  

(Methods Based on The Artificial Intelligence, Machine Learning and Heuristic Algorithms)

7.  Neural Networks: They present a brain metaphor for information processing. These models are biologically inspired computational models. They consist of an interconnected group of artificial neurons and process information using computation approach.

8.  Decision Trees: The decision tree is a tree-shaped diagram that represents classification or regression models. it divides a data set into smaller and smaller sub data sets while at the same time a related decision tree is continuously developed.

9.  Evolutionary Algorithms: A common concept that combines many different types of data analysis using evolutionary algorithms. Most popular of them are: genetic algorithms, genetic programming, and co-evolutionary algorithms.

Long, but very educative post by Pranab Ghosh that describes discriminant analysis and then provides an implementation for calculating the probability of visitors’ conversion using the Bayesian discriminant with Hadoop:

You have lots of visitors on your eCommerce web site and obviously you would like most of them to convert. By conversion, I mean buying   your product or service. It could also mean the visitor taking  an action, which potentially could financially benefit the business e.g., opening an account or signing up for email new letter. In this post, I will cover some predictive data mining techniques that may facilitate higher conversion rate.