Linear discriminant analysis, OLAP cubes and Bubble charts
Linear discriminant analysis
Linear discriminant analysis (LDA) method is used in statistics, pattern recognition and machine learning to find a linear combination of features which characterize or separate two or more classes of objects or events.
· Bankruptcy prediction – LDA was used to determine which firms went into bankruptcy and those that survived based on predictions made using financial ratios.
· Face recognition – In computerised face recognition, each face is represented by a large number of pixel values. Linear discriminant analysis is primarily used here to reduce the number of features to a more manageable number before classification.
· Marketing – In marketing, discriminant analysis was once often used to determine the factors which distinguish different types of customers and/or products on the basis of surveys or other forms of collected data.Logistic regression or other methods are now more commonly used.
An OLAP cube (for online analytical processing) is a data structure that allows fast analysis of data. It can also be defined as the capability of manipulating and analyzing data from multiple perspectives. The arrangement of data into cubes overcomes some limitations of relational databases.
There are three reasons for adding a cube to your solution:
· Performance – A cube’s structure and pre-aggregation allows it to provide very fast responses to queries that would have required reading, grouping and summarizing millions of rows of relational data.
· Drill down functionality – Many reporting software tools will automatically allow drilling up and down on dimensions with the data source is an OLAP cube. Some tools, like IBM Cognos’ Dimensionally Modeled Relational model will allow you to use their product on a relational source and drill down as if it were OLAP but you would not have the performance gains you would enjoy from a cube.
· Availability of software tools – Some client software reporting tools will only use an OLAP data source for reporting. These tools are designed for multi-dimensional analysis and use MDX behind the scenes to query the data.
A bubble chart is a type of chart where each plotted entity is defined in terms of three distinct numeric parameters. Bubble charts can facilitate the understanding of the social, economical, medical, and other scientific relationships.
Use of bubble charts
· The single best example of good use of bubbles is a scatterplot demonstrating some relationship between the x and y variables. The bubbles are added to demonstrate some size metric to help show whether the relationship holds as well for big or small entities of analysis.
· Another good use of bubbles is to help prioritize decisions coming from diagnostic-type analyses, for example, if we have a growth vs. Profitability matrix.
Group – Marketing 3
Author of the Article – Rajiv Venugopal