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How to measure similarity for multiple categorical data sets?

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Abstract

How to measure similarity or distance for multiple categorical data? It is an important step for Data Mining and Knowledge Management process to measure similarity or distance between objects appropriately. Measurements for continuous data have been well-defined and relatively easy to be calculated. However, the notion of similarity for categorical data is not simple, since categorical data usually is not simply translated into the numerical format, and they also have their own priority with structures and data distribution. In this paper, we propose a new measure for multiple categorical data sets using data distribution. Our new measure, MCSM (Multiple Categorical Similarity Measure), can solve conventional drawbacks of multiple categorical data sets successfully in which we prove the verification of our measure with mathematical proofs and experimentation. The experimental result shows that our measure is powerful for multiple categorical data sets with proper data distributions.

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Acknowledgments

This work was supported by Inha University, Seokyeong University and the National Research Foundation of Korea(NRF) Grant funded by the Korean Government(MOE) (NRF-2013R1A1A2012887)

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, INHA University, Incheon, South Korea

    Simon Soon-Hyoung Park, Justin JongSu Song, James Jung-Hoon Lee & Wookey Lee

  2. Department of Industrial Engineering, Seokyeong University, Seoul, South Korea

    Sangbok Ree

Authors
  1. Simon Soon-Hyoung Park
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  2. Justin JongSu Song
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  3. James Jung-Hoon Lee
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  4. Wookey Lee
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  5. Sangbok Ree
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Corresponding author

Correspondence to Wookey Lee.

Appendix: Experimental results on ACM classification

Appendix: Experimental results on ACM classification

A1 INTRODUCTORY AND SURVEY 2

C2 COMPUTER-COMMUNICATION NETWORKS 2

C20 Security and protection (eg, firewalls) 1

C21 Network Architecture and Design 3

C22 Network Protocols 4

C24 Distributed Systems 13

C25 Internet1

C26 Standards (eg, TCP/IP) 1

C2m Miscellaneous 1

C4 Design studies 14

D15 Object-oriented Programming 1

D2 SOFTWARE ENGINEERING 1

D20 Protection mechanisms 2

D21 Requirements/Specifications 2

D211 Information hiding 4

D212 Distributed objects 6

D213 Reuse models 2

D22 Design Tools and Techniques 6

D24 Formal methods 4

D25 Testing and Debugging 6

D26 Programming Environments 3

D28 Performance measures 8

D29 Management 5

D3 PROGRAMMING LANGUAGES 1

D31 Formal Definitions and Theory 4

D32 Language Classifications 2

D33 Frameworks 7

D34 Retargetable compilers 9

D46 Security and Protection 3

E1 Graphs and networks 5

E2 DATA STORAGE REPRESENTATIONS 2

E4 Error control codes 3

F11 Models of Computation 2

F2 ANALYSIS OF ALGORITHMS AND PROBLEM COMPLEXITY 1

F20 General 5

F22 Nonnumerical Algorithms and Problems 1

F32 Semantics of Programming Languages 2

F43 Formal Languages 2

G16 Optimization 1

G21 Combinatorial algorithms 2

G22 Network problems 3

G3 PROBABILITY AND STATISTICS 8

H0 GENERAL 1

H1 MODELS AND PRINCIPLES 2

H10 General 2

H11 Systems and Information Theory 7

H12 Human factor 2

H1m Miscellaneous 1

H20 General 1

H21 Schema and subschema 3

H23 Data description languages (DDL) 6

H24 Query processing 11

H27 Security, integrity, and protection 2

H28 Database applications 16

H2m Miscellaneous 1

H3 INFORMATION STORAGE AND RETRIEVAL 4

H30 General 4

H31 Content Analysis and Indexing 19

H32 Information Storage 1

H33 Information Search and Retrieval 119

H34 Systems and Software 22

H35 On-line Information Services 9

H36 Library Automation 37

H37 Dissemination 1

H3m Miscellaneous 4

H4 INFORMATION SYSTEMS APPLICATIONS 2

H40 General 2

H43 Communications Applications 9

H4m Miscellaneous 20

H5 INFORMATION INTERFACES AND PRESENTATION 1

H51 Multimedia Information Systems 3

H52 User Interfaces 21

H53 Group and Organization Interfaces 23

H54 Hypertext/Hypermedia 17

Hm MISCELLANEOUS 4

I2 ARTIFICIAL INTELLIGENCE 1

I20 General 1

I22 Program verification 1

I23 Deduction and Theorem Proving 2

I24 Knowledge Representation Formalisms and Methods 20

I26 Learning 11

I27 Text analysis 10

I28 Graph and tree search strategies 1

I2m Miscellaneous 1

I2n Distributed Artificial Intelligence 3

I36 Interaction techniques 1

I4 IMAGE PROCESSING AND COMPUTER VISION 1

I51 Neural nets 1

I52 Classifier design and evaluation 5

I53 Algorithms 1

I54 Text processing 2

I65 Model Development 2

I6m Miscellaneous 1

I7 DOCUMENT AND TEXT PROCESSING 1

I72 Document Preparation 3

I75 Document analysis 1

I7m Miscellaneous 2

J0 GENERAL 3

J2 Chemistry 2

J4 SOCIAL AND BEHAVIORAL SCIENCES 15

J5 Performing arts (eg, dance, music) 1

Jm MISCELLANEOUS 1

K31 Computer Uses in Education 5

K41 Public Policy Issues 4

K42 Assistive technologies for persons with disabilities 2

K43 Organizational Impacts 3

K44 Electronic Commerce 11

K4m Miscellaneous 1

K52 Governmental Issues 1

K63 Software Management 1

K64 System Management 2

K65 Security and Protection 12

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Park, S.SH., Song, J.J., Lee, J.JH. et al. How to measure similarity for multiple categorical data sets?. Multimed Tools Appl 74, 3489–3505 (2015). https://doi.org/10.1007/s11042-014-1914-5

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  • DOI: https://doi.org/10.1007/s11042-014-1914-5

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