Association-Based Dissimilarity Measures for Categorical Data: Limitation and Improvement

2006 ◽  
pp. 493-498
Author(s):  
Si Quang Le ◽  
Tu Bao Ho ◽  
Le Sy Vinh
Keyword(s):  
Categorical Data ◽  
Dissimilarity Measures

scholarly journals A Global-Relationship Dissimilarity Measure for thek-Modes Clustering Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Hongfang Zhou ◽  
Yihui Zhang ◽  
Yibin Liu
Keyword(s):  
Categorical Data ◽  
Clustering Algorithm ◽  
Real Data ◽  
Dissimilarity Measure ◽  
Data Sets ◽  
Dissimilarity Measures

Thek-modes clustering algorithm has been widely used to cluster categorical data. In this paper, we firstly analyzed thek-modes algorithm and its dissimilarity measure. Based on this, we then proposed a novel dissimilarity measure, which is named as GRD. GRD considers not only the relationships between the object and all cluster modes but also the differences of different attributes. Finally the experiments were made on four real data sets from UCI. And the corresponding results show that GRD achieves better performance than two existing dissimilarity measures used ink-modes and Cao’s algorithms.

scholarly journals Possibilistic Fuzzy Clustering for Categorical Data Arrays Based on Frequency Prototypes and Dissimilarity Measures

2017 ◽  
Vol 9 (5) ◽  
pp. 55-61 ◽  
Author(s):  
Zhengbing Hu ◽  
◽  
Yevgeniy V. Bodyanskiy ◽  
Oleksii K. Tyshchenko ◽  
Viktoriia O. Samitova
Keyword(s):  
Fuzzy Clustering ◽  
Categorical Data ◽  
Dissimilarity Measures

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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