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sented a semi-supervised clustering ensemble method to combine
multiple semi-supervised clustering components for ensuring that
the given constraints cannot be violated in the final clustering
result.
The authors in [22,23] have recently investigated the combina-
tion of semi-supervised clustering and clustering ensemble. Com-
pared with the random selection of constraints, they utilized the
active learning technique or bounded constraints selection to ob-
tain more informative and valuable constraints, which need query-
ing from oracles or intervention with users. However, it is not
realistic to ensure that the entire requirement for constraints is
satisfied by the feedback from the real world.
This paper explores a combination of semi-supervised cluster-
ing and consensus clustering in IDSS at a different perspective.
Thanks to the sensitivity of assignment order for many semi-super-
vised clustering algorithms [15,24], the clustering ensemble in the
form of the co-association matrix gets a new assignment order for
data objects. An improved version of Cop-Kmeans algorithm is
then employed to generate multiple clustering components [25].
The paper proposes a novel constrained self-organizing map as
the consensus function to obtain the final partition. The case study
shows that the improved methods can support better decision
making in IDSS.
The rest of the paper is organized as follows: Section 2 introduces
the system framework, including data-driven DSS based on data
mining and the consensus clustering. Section 3 outlines the
K-means algorithm, Cop-Kmeans algorithm and an improved Cop-
Kmeans (ICop-Kmeans) algorithm. Section 4 presents a new assign-
ment order based on the weighted co-association matrix. Section 5
proposes a novel semi-supervised clustering ensemble. Section 6
illustrates a case study to validate that clustering is an important
component in IDSS. Section 7 shows the experimental evaluation.
Section 8 concludes the paper with further research topics.
2. The architecture of data-driven DSS based on data mining
2.1. A framework of data-driven DSS
Today’s real world decision issues have become more compli-
cated, time consuming and difficult for managers, leading to mod-
ern approaches to the decision-making processes. Such a process
involves more factors and aspects like data mining and OLAP than
traditional decision making models. Fig. 1 shows a framework of
data-driven DSS, in which data mining (e.g., clustering) as an
important component in IDSS supports better decision making.
2.2. A framework of the consensus clustering
Fig. 2 shows a framework of consensus clustering based on the
constrained SOM and improved Cop-Kmeans ensemble. The first
phase consists of H clustering components using the improved
Cop-Kmeans (ICop-Kmeans) algorithm with different initial
Clustering
Component
1
Clustering
Component
H
Clustering
Component
2
Original Dataset X
ICop-Kmeans
New Feature Space Matrix A
Final Clustering Result '
π
Calculation of Certainty
Reassign order
Cop-SOM-E
Fig. 2. A framework of the consensus clustering based on the constrained SOM and ICOP-Kmeans ensemble.
Y. Yang et al. / Knowledge-Based Systems 32 (2012) 101–115
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