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 | |
| 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 |
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All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Computer Science Applications
- Information Systems
Cite this
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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.
ER -