Analyzing Sequential Categorical Data on Dyadic Interaction

1989 ◽  
pp. 104-116 ◽  
Author(s):  
Ajith Kumar ◽  
William R. Dillon
Keyword(s):  
Categorical Data ◽  
Dyadic Interaction

Analyzing sequential categorical data on dyadic interaction: A comment on Gottman.

1982 ◽  
Vol 91 (2) ◽  
pp. 393-403 ◽  
Author(s):  
Paul D. Allison ◽  
Jeffrey K. Liker
Keyword(s):  
Categorical Data ◽  
Dyadic Interaction

Analyzing sequential categorical data on dyadic interaction: A latent structure approach.

1983 ◽  
Vol 94 (3) ◽  
pp. 564-583 ◽  
Author(s):  
William R. Dillon ◽  
Thomas J. Madden ◽  
Ajith Kumar
Keyword(s):  
Categorical Data ◽  
Latent Structure ◽  
Dyadic Interaction ◽  
Structure Approach

Dyadic interaction patterns.

1985 ◽  
Author(s):  
Robert A. Ravich
Keyword(s):  
Dyadic Interaction ◽  
Interaction Patterns

Non-Mode Clustering of Categorical Data with Attributes Weighting

2014 ◽  
Vol 24 (11) ◽  
pp. 2628-2641 ◽  
Author(s):  
Li-Fei CHEN ◽  
Gong-De GUO
Keyword(s):  
Categorical Data

Machine Learning Based Predictive Action on Categorical Non-Sequential Data

2020 ◽  
Vol 13 (5) ◽  
pp. 1020-1030
Author(s):  
Pradeep S. ◽  
Jagadish S. Kallimani
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Categorical Data ◽  
Numerical Data ◽  
Processing Technique ◽  
Machine Learning Algorithms ◽  
Sequential Data ◽  
Industry Standard ◽  
Robust Model ◽  
Future Work

Background: With the advent of data analysis and machine learning, there is a growing impetus of analyzing and generating models on historic data. The data comes in numerous forms and shapes with an abundance of challenges. The most sorted form of data for analysis is the numerical data. With the plethora of algorithms and tools it is quite manageable to deal with such data. Another form of data is of categorical nature, which is subdivided into, ordinal (order wise) and nominal (number wise). This data can be broadly classified as Sequential and Non-Sequential. Sequential data analysis is easier to preprocess using algorithms. Objective: The challenge of applying machine learning algorithms on categorical data of nonsequential nature is dealt in this paper. Methods: Upon implementing several data analysis algorithms on such data, we end up getting a biased result, which makes it impossible to generate a reliable predictive model. In this paper, we will address this problem by walking through a handful of techniques which during our research helped us in dealing with a large categorical data of non-sequential nature. In subsequent sections, we will discuss the possible implementable solutions and shortfalls of these techniques. Results: The methods are applied to sample datasets available in public domain and the results with respect to accuracy of classification are satisfactory. Conclusion: The best pre-processing technique we observed in our research is one hot encoding, which facilitates breaking down the categorical features into binary and feeding it into an Algorithm to predict the outcome. The example that we took is not abstract but it is a real – time production services dataset, which had many complex variations of categorical features. Our Future work includes creating a robust model on such data and deploying it into industry standard applications.

Categorical Data Analysis for Geographers and Environmental Scientists

1986 ◽  
Vol 62 (2) ◽  
pp. 192 ◽  
Author(s):  
Joel L. Horowitz ◽  
Neil Wrigley
Keyword(s):  
Data Analysis ◽  
Categorical Data ◽  
Categorical Data Analysis
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