Extracting quality relations from categorical data using modified Qube algorithm

Sayan Nanda; Rajesh Gopakumar; Sanjay Singh

doi:10.1109/ICACCI.2017.8125915

Extracting quality relations from categorical data using modified Qube algorithm

Sayan Nanda, Rajesh Gopakumar, Sanjay Singh

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Partial derivatives are used to describe the trend of a dependent categorical variable. This is used to extract quality relations from categorical data through the definition of a Probabilistic Discrete Qualitative Partial Derivative (PDQ PD). This has been covered in the Qube algorithm. However, on analysis of the current method, it is found that a large amount of the time is taken in the attribute selection phase. The objective of this paper is to improve the efficiency of this algorithm in particular by decreasing the time taken for attribute selection. In this paper we have modified the Qube algorithm, the modified algorithm is able to reduce the time taken by searching the data set for rows with the same variable values. The ordering is then replicated in each case. This has been found to improve the efficiency of the algorithm especially in data sets where there are multiple items with the same values.

Original language	English
Title of host publication	2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	651-655
Number of pages	5
Volume	2017-January
ISBN (Electronic)	9781509063673
DOIs	https://doi.org/10.1109/ICACCI.2017.8125915
Publication status	Published - 30-11-2017
Event	2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 - Manipal, Mangalore, India Duration: 13-09-2017 → 16-09-2017

Conference

Conference	2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
Country	India
City	Manipal, Mangalore
Period	13-09-17 → 16-09-17

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Computer Science Applications
Information Systems

Access to Document

10.1109/ICACCI.2017.8125915

Fingerprint Dive into the research topics of 'Extracting quality relations from categorical data using modified Qube algorithm'. Together they form a unique fingerprint.

Cite this

Nanda, S., Gopakumar, R., & Singh, S. (2017). Extracting quality relations from categorical data using modified Qube algorithm. In 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 (Vol. 2017-January, pp. 651-655). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICACCI.2017.8125915

@inproceedings{0d9ba0030f4140589a1938f7b89c2c43,

title = "Extracting quality relations from categorical data using modified Qube algorithm",

abstract = "Partial derivatives are used to describe the trend of a dependent categorical variable. This is used to extract quality relations from categorical data through the definition of a Probabilistic Discrete Qualitative Partial Derivative (PDQ PD). This has been covered in the Qube algorithm. However, on analysis of the current method, it is found that a large amount of the time is taken in the attribute selection phase. The objective of this paper is to improve the efficiency of this algorithm in particular by decreasing the time taken for attribute selection. In this paper we have modified the Qube algorithm, the modified algorithm is able to reduce the time taken by searching the data set for rows with the same variable values. The ordering is then replicated in each case. This has been found to improve the efficiency of the algorithm especially in data sets where there are multiple items with the same values.",

author = "Sayan Nanda and Rajesh Gopakumar and Sanjay Singh",

year = "2017",

month = nov,

day = "30",

doi = "10.1109/ICACCI.2017.8125915",

language = "English",

volume = "2017-January",

pages = "651--655",

booktitle = "2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 ; Conference date: 13-09-2017 Through 16-09-2017",

}

Nanda, S, Gopakumar, R & Singh, S 2017, Extracting quality relations from categorical data using modified Qube algorithm. in 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017. vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 651-655, 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, Manipal, Mangalore, India, 13-09-17. https://doi.org/10.1109/ICACCI.2017.8125915

Extracting quality relations from categorical data using modified Qube algorithm. / Nanda, Sayan; Gopakumar, Rajesh; Singh, Sanjay.

2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 651-655.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Extracting quality relations from categorical data using modified Qube algorithm

AU - Nanda, Sayan

AU - Gopakumar, Rajesh

AU - Singh, Sanjay

PY - 2017/11/30

Y1 - 2017/11/30

N2 - Partial derivatives are used to describe the trend of a dependent categorical variable. This is used to extract quality relations from categorical data through the definition of a Probabilistic Discrete Qualitative Partial Derivative (PDQ PD). This has been covered in the Qube algorithm. However, on analysis of the current method, it is found that a large amount of the time is taken in the attribute selection phase. The objective of this paper is to improve the efficiency of this algorithm in particular by decreasing the time taken for attribute selection. In this paper we have modified the Qube algorithm, the modified algorithm is able to reduce the time taken by searching the data set for rows with the same variable values. The ordering is then replicated in each case. This has been found to improve the efficiency of the algorithm especially in data sets where there are multiple items with the same values.

AB - Partial derivatives are used to describe the trend of a dependent categorical variable. This is used to extract quality relations from categorical data through the definition of a Probabilistic Discrete Qualitative Partial Derivative (PDQ PD). This has been covered in the Qube algorithm. However, on analysis of the current method, it is found that a large amount of the time is taken in the attribute selection phase. The objective of this paper is to improve the efficiency of this algorithm in particular by decreasing the time taken for attribute selection. In this paper we have modified the Qube algorithm, the modified algorithm is able to reduce the time taken by searching the data set for rows with the same variable values. The ordering is then replicated in each case. This has been found to improve the efficiency of the algorithm especially in data sets where there are multiple items with the same values.

UR - http://www.scopus.com/inward/record.url?scp=85042933707&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85042933707&partnerID=8YFLogxK

U2 - 10.1109/ICACCI.2017.8125915

DO - 10.1109/ICACCI.2017.8125915

M3 - Conference contribution

AN - SCOPUS:85042933707

VL - 2017-January

SP - 651

EP - 655

BT - 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017

Y2 - 13 September 2017 through 16 September 2017

ER -