The partitional clustering concept started with K-means algorithm which was published in
1957. Since then many classical partitional clustering algorithms have been reported based …

Data clustering is a popular unsupervised data mining tool that is used for partitioning a
given dataset into homogeneous groups based on some similarity/dissimilarity metric …

Clustering ensemble has received considerable research interest and led to a proliferation
of studies, since it has great capabilities to combine multiple base clusters to generate a …

In real-world problems we encounter situations where patterns are described by blocks
(families) of features where each of these groups comes with a well-expressed semantics …

Clustering algorithms with attribute weighting have gained much attention during the last
decade. However, they usually optimize a single-objective function that can be a limitation to …

In the field of computational bioinformatics, identifying a set of genes which are responsible
for a particular cellular mechanism, is very much essential for tasks such as medical …

In this paper, a new multi-objective genetic programming (GP) with a diversity preserving
mechanism and a real number alteration operator is presented and successfully used for …

Cluster ensemble techniques aim to combine the outputs of multiple clustering algorithms to
obtain a single consensus partitioning. The current paper reports about the development of …

Clustering is a data mining technique widely used to find similar group of data. A better
cluster always have most similar data while the elements from the different clusters are …

Sažetak In this paper, we implemented a diagnostic system for vibration faults that occur on
the PUMA helicopter gearbox. We used an approach based on the joint use of the Order …