Proceedings of the 8th International Conference on Intellectual ...

2.2 Sequencing association rules
Ridha Derrouiche et al.
To discovery sequencing association rules (Agrawal, 1993) is a noteworthy data mining approach.
There are some evidences <strong>of</strong> <strong>the</strong> trend <strong>of</strong> association rule in many business researches. This review
indicated that association rule techniques used in various business, but ra<strong>the</strong>r rely on <strong>the</strong> popular
used in supply chain management in performance measurement have not done very much. For
example: depend on organizational strategies were mainly concentrated on improvement <strong>of</strong> customer
service levels as well as reduction <strong>of</strong> operational costs in order to maintain pr<strong>of</strong>it margins. Therefore,
Ko, et al. (2010) summarized <strong>the</strong> findings by a systematic review <strong>of</strong> existing research papers
concerning <strong>the</strong> application <strong>of</strong> s<strong>of</strong>t computing techniques to supply chain management. The amount <strong>of</strong>
studies in <strong>the</strong> supply chain management area using s<strong>of</strong>t computing approaches rose significantly and
reached a peak in 2008, while some areas in supply chain management that had rarely been exposed
in existing papers, such as customer relationship management and reverse logistics. So <strong>the</strong>y can
bring <strong>the</strong> association rule data mining techniques to applications in <strong>the</strong> supply chain for example. The
greater <strong>the</strong> uncertainties in supply and demand, globalization <strong>of</strong> <strong>the</strong> market in complex international
supply network relationships have led to higher exposure to risks in <strong>the</strong> supply chain. Therefore, He
and Song (2009) used association rules application for managing supply chain risk. Supplier selection
is not only a significant work in supply chain management but also a complex decision making
problem which includes both qualitative and quantitative factors. Xu and Lin (2009) viewed as <strong>the</strong>
problem <strong>of</strong> ranking <strong>the</strong> candidate supplier and mining a large <strong>of</strong> database <strong>of</strong> shipment by using
association rule mining. The paper has employed a numerical example for <strong>the</strong> integrated method for
suitable supplier set.
2.3 K-Means algorithm
K-Means is one <strong>of</strong> <strong>the</strong> modest unsupervised learning algorithms, solved <strong>the</strong> famous clustering
problem. The procedure like this a basic practice to establish a given data set through a certain
number <strong>of</strong> clusters (assume k clusters) fixed a priori (Ben-David et al, 2007 and Borah and Ghose,
2009). The chief idea is to outline k centroids, one for each cluster. These centroids should be placed
in a cunning method on <strong>the</strong> ground <strong>of</strong> dissimilar site causes different result. So, <strong>the</strong> better choice is to
place <strong>the</strong>m as much as possible far away from each o<strong>the</strong>r. The next step is to take each point
belonging to a given data set and associate it to <strong>the</strong> nearest centroid. When no point is pending, <strong>the</strong>
first step is completed and an early group age is done. At this point it is necessary to re-calculate k
new centroids as bar centers <strong>of</strong> <strong>the</strong> clusters resulting from <strong>the</strong> previous step. After obtaining <strong>the</strong>se k
new centroids, a new binding has to be done between <strong>the</strong> same data set points and <strong>the</strong> nearest new
centroid. A loop has been generated. As a result <strong>of</strong> this loop, one may notice that <strong>the</strong> k centroids
change <strong>the</strong>ir location step by step until no more changes are done. In o<strong>the</strong>r words centroids do not
move any more. Finally, this algorithm aims at minimizing an objective function, in this case a squared
error function
2.4 Particle swarm intelligence
Particle swarm optimization (PSO) is a social behavior based stochastic optimization method
developed by (Eberhart and Kennedy in 1995), At each step, each <strong>of</strong> <strong>the</strong>se particle locations is
recorded to get a fitness value based on how well it can resolves <strong>the</strong> problem. By means <strong>of</strong> <strong>the</strong> local
best position (Lbest : Pid) and <strong>the</strong> global best position (Gbest : Pgd), a novel velocity for each particle is
updated by equation 1 Such f1 and f2 are called <strong>the</strong> coefficient <strong>of</strong> inertia, cognitive and society
revision correspondingly. The rand ( ) stands uniformly distributed random numbers in [0,1]. The term
Vid is restricted to <strong>the</strong> possible rang <strong>of</strong> Vmax . If <strong>the</strong> velocity disrupts this limit, it will be set at its proper
limit. The concept <strong>of</strong> <strong>the</strong> updated velocity is illustrated in Figure 2. Varying velocity allows every
particle to hunt around its distinct best position and global best position. Depended on <strong>the</strong> updated
velocities, each particle changes its position according to equation 2
( )
( )
new old V = V + c 1⋅rand 1 P − X + c 2⋅rand 2 P −X
new old new
X = X + V
id id id id gd id
id id id
(1)
(2)
Every particle is updated; <strong>the</strong> fitness value <strong>of</strong> each particle is recalculated. Providing that <strong>the</strong> fitness
value <strong>of</strong> <strong>the</strong> new particle is superior to those <strong>of</strong> local best, formerly <strong>the</strong> local best will be substituted
with <strong>the</strong> new particle. Supporting <strong>the</strong> fitness value <strong>of</strong> <strong>the</strong> new particle is higher than those <strong>of</strong> global
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