Abstract
We will focus in this paper on the competitive intelligence problem which deals with the competitive environment of a company. Our purpose is to predict and anticipate the action of its competitor. We are talking here about a context of reasoning under uncertainty. All existed works define the concept of competitive intelligence and propose a scheme for the competitive intelligence process and its stages, but there is no work, at the best of our knowledge, that touched the practical aspect of the field or developed a complete competitive intelligence solution that can be delivered to the decision maker, which makes the originality of our work. To motivate the research, we will address a competitive practical case in the field of telecommunications. In this paper we propose a competitive intelligence solution composed by two steps: actions association using k-modes algorithm which has the capability to deal with nominal data, and actions generation using rough set theory which has the capability to deal with inexact data and drive rules from it.
| Original language | English |
|---|---|
| Pages (from-to) | 597-606 |
| Number of pages | 10 |
| Journal | Procedia Computer Science |
| Volume | 96 |
| DOIs | |
| Publication status | Published - 2016 |
Fingerprint
Keywords
- and action prediction
- Competitive intelligence
- intelligente method for prediction
- reasonning under uncertainty
- rough set theory
- rules generation
ASJC Scopus subject areas
- Computer Science(all)
Cite this
A Competitive Intelligence Solution to Predict Competitor Action Using K-modes Algorithm and Rough Set Theory. / Ben Sassi, Dhekra; Frini, Anissa; Ben Abdessalem Karaa, Wahiba; Kraiem, Naoufel.
In: Procedia Computer Science, Vol. 96, 2016, p. 597-606.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A Competitive Intelligence Solution to Predict Competitor Action Using K-modes Algorithm and Rough Set Theory
AU - Ben Sassi, Dhekra
AU - Frini, Anissa
AU - Ben Abdessalem Karaa, Wahiba
AU - Kraiem, Naoufel
PY - 2016
Y1 - 2016
N2 - We will focus in this paper on the competitive intelligence problem which deals with the competitive environment of a company. Our purpose is to predict and anticipate the action of its competitor. We are talking here about a context of reasoning under uncertainty. All existed works define the concept of competitive intelligence and propose a scheme for the competitive intelligence process and its stages, but there is no work, at the best of our knowledge, that touched the practical aspect of the field or developed a complete competitive intelligence solution that can be delivered to the decision maker, which makes the originality of our work. To motivate the research, we will address a competitive practical case in the field of telecommunications. In this paper we propose a competitive intelligence solution composed by two steps: actions association using k-modes algorithm which has the capability to deal with nominal data, and actions generation using rough set theory which has the capability to deal with inexact data and drive rules from it.
AB - We will focus in this paper on the competitive intelligence problem which deals with the competitive environment of a company. Our purpose is to predict and anticipate the action of its competitor. We are talking here about a context of reasoning under uncertainty. All existed works define the concept of competitive intelligence and propose a scheme for the competitive intelligence process and its stages, but there is no work, at the best of our knowledge, that touched the practical aspect of the field or developed a complete competitive intelligence solution that can be delivered to the decision maker, which makes the originality of our work. To motivate the research, we will address a competitive practical case in the field of telecommunications. In this paper we propose a competitive intelligence solution composed by two steps: actions association using k-modes algorithm which has the capability to deal with nominal data, and actions generation using rough set theory which has the capability to deal with inexact data and drive rules from it.
KW - and action prediction
KW - Competitive intelligence
KW - intelligente method for prediction
KW - reasonning under uncertainty
KW - rough set theory
KW - rules generation
UR - http://www.scopus.com/inward/record.url?scp=84988841203&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84988841203&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2016.08.240
DO - 10.1016/j.procs.2016.08.240
M3 - Article
AN - SCOPUS:84988841203
VL - 96
SP - 597
EP - 606
JO - Procedia Computer Science
JF - Procedia Computer Science
SN - 1877-0509
ER -