Introduction
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western university upload
Showing posts with label Data analysis. Show all posts
Showing posts with label Data analysis. Show all posts
Saturday, October 24, 2015
Structural Equation Modeling
Introduction
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western university upload
Sunday, May 27, 2012
Regression Models with Qualitative Dependent Variables
Regression Models with Qualitative Dependent Variables
Authors
1. Probit Models
2. Logit Models
3. Discriminant Analysis
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Applications
- The Comparison Logit and Probit Regression Analyses in
Estimating the Strength of Gear Teeth
European Journal of Scientific Research
ISSN 1450-216X Vol.27 No.4 (2009), pp.548-553
http://www.eurojournals.com/ejsr.htm - Probit Regression Example, Jim, Albert, 2007
- http://bayes.bgsu.edu/bcwr/vignettes/probit_regression.pdf
- Logistic and Probit Models - Difference
- http://www.stat.ufl.edu/CourseINFO/STA6167/logistregSFLM.pdf
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Saturday, May 5, 2012
Cluster Analysis
Cluster Analysis
Authors
Using cluster analysis, persons or objects can be categorized into groups having similar measurments of number of characteristics or attributes. Cluster analysis techniques form groups such that the simimilarity among the group members is maximized.
The similarity measures used in clustering are:
Distance measure
Correlation coefficient
Association coefficient
Distance measure
For distance measure, the Euclidean distance between two points is used. Actually, the distance measure is commented as dissimilarity measure also.
In two dimensional space, if the coordinates of two points are (x1,y1) and (x2, y2), then the distance between two points is
d12 = SQRT[ (x2-x1)2 + (y2-y1)2]
In the case of a cluster of points, the representative point of the cluster for calculating the distance between a cluster and a point or another cluster is the centroid.
The coordinates of the centroid are calculated by the formula:
X = Sum of xi (i = 1 to n)/n
Where X = x coordinate of the centroid
n = number of points in the centroid
Association coefficient
Where number of attributes are there for each object, a value of zero is assigned for an attribute in case of an object if it is not present in the object. If the attribute is present value of one assigned. For each object, the total score is found out and it is termed as the association coefficient.
Clustering Techniques
Hierarchical clustering and non-nierarchical clustering is a classification of clustering techniques.
In hierarchical clustering there is bottom up approach termed as agglomerative method. In this methods, all objects are initially treated as independent clusters and clustering starts with reduction in number of clusters till one cluster of all objects is formed. No doubt at some point in the process, there will be best cluster output.
In the top-down approach termed as divisive method, at the start all objects are included in a single cluster and clustering process breaks them into more number of clusters.
In non hierarchical techniques, some inital solution is used and clustering takes place. In these techniques, objects are allowed to change clusters at the various steps in the process.
Some of the popular hierarchical clustering techniques are:
Single linkage clustering method
Complete linkage clustering method
Average linkage clustering method
Ward's method
Cnetroid method
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Further References
Hierarchical Clustering Methods - Presentation in PDF file
Research papers
Multi parameter Hierarchical Clustering Methods, 2009
Comments
Tuesday, February 21, 2012
Grey Theory and Grey Relational Analysis - A Brief Introduction
Nowadays, the black is represented, as lack of information, but the white is full of information. Thus, the information that is either incomplete or undetermined, is called Grey.
A system having incomplete information is called Grey system. The Grey number in Grey system represents a number with less complete information. The Grey element represents an element with incomplete information. The Grey relation is the relation with incomplete information. Those three terms are the typical symbols and features for Grey system and Grey phenomenon . There are several aspects for the theory of Grey system:
1. Grey generation: This is data processing to supplement information. It is aimed to process those complicate and tedious data to gain a clear rule, which is the whitening of a sequence of numbers.
2. Grey modeling: This is done by step 1 to establish a set of Grey variation equations and Grey differential equations, which is the whitening of the model.
3. Grey prediction: By using the Grey model to conduct a qualitative prediction, this is called the whitening of development.
4. Grey decision: A decision is made under imperfect countermeasure and unclear situation, which is called the whitening of status.
5. Grey relational analysis: Quantify all influences of various factors and their relation, which is called the whitening of factor relation.
6. Grey control: Work on the data of system behavior and look for any rules of behavior development to predict future’s behavior, the prediction value can be fed back into the system in order to control the system.
Wu, H.H., “The Introduction of Grey Analysis,” Gauli Publishing Co., Taipei, 1996.
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Papers
Grey system theory-based models in time series prediction, 2010 paper in journal Expert Systems With Applications
Combined Grey Relational Analysis and methods for optimization of process parameters in cylindrical grindingPaper uploaded on academica.edu, 2009
Multi-attribute Decision Making Model by Applying Grey Numbers
Informatica, 2009
(Has good explanation of white, grey and black)
Airline Network Optimization using Grey Theory (2007, Research Paper by IIT Bombay faculty and PG student)
A Novel Prediction Method for Stock Index Applying Grey Theory and Neural Networks, 2008 OR Symposium paper
Grey Information: Theory and Practical Applications, 2006
Implementing Grey Model and Value Analysis aind QFD Proces to improve customer satisfaction
(Application to Juanda International Airport - Surabaya, Indonesia)
Paper uploaded on academica.edu
Seminal paper
"The Control Problems of Grey Systems," Deng Julong (of China), The Journal of Systems and Control Letters, 1982.
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http://knol.google.com/k/narayana-rao-k-v-s-s/-/2utb2lsm2k7a/3531
Narayana Rao - 08 Dec 2010