This paper describes a graph clustering algorithm that aims to minimize the normalized cut criterion and has a model order selection procedure. The performance of the proposed algorithm is comparable to spectral approaches in terms of minimizing normalized cut. However, unlike spectral approaches, the proposed algorithm scales to graphs with millions of nodes and edges. The algorithm consists o...

In the era of smart manufacturing, autonomous mobile robots have become affordable for numerous companies, although fleet management remains a challenging problem. A novel approach is proposed, supporting solution vehicle assignment The method relies on adaptive workstation clustering that considers not only complex environment layout, but also main characteristics material flow. technique comb...

UNLABELLED Pyrosequencing technologies are frequently used for sequencing the 16S ribosomal RNA marker gene for profiling microbial communities. Clustering of the produced reads is an important but time-consuming task. We present Dynamic Seed-based Clustering (DySC), a new tool based on the greedy clustering approach that uses a dynamic seeding strategy. Evaluations based on the normalized mutu...

One difficult problem we are often faced with in clustering analysis is how to choose the number of clusters. In this paper, we propcse to choose the number of clusters by optimizing the Bq2yesian information criterion (BIC), a model selection criierion in the statistics literature. We develop a termination criterion for the hierarchical clustering methods which optimizes the BIC criterion in a...

Several recent deep neural networks experiments leverage the generalist-specialist paradigm for classification. However, no formal study compared the performance of different clustering algorithms for class assignment. In this paper we perform such a study, suggest slight modifications to the clustering procedures, and propose a novel algorithm designed to optimize the performance of of the spe...

Authors propose new genetic algorithm for solving the planar p-median location problem and k-means clustering problem. The ideas of the algorithm are based on the genetic algorithm with greedy heuristic for the p-median problem on networks and information bottleneck (IB) clustering algorithms. The proposed algorithm uses the standard k-means procedure or any other similar algorithm for local se...

association rules are among important techniques in data mining which are used for extracting hidden patterns and knowledge in large volumes of data. association rules help individuals and organizations take strategic decisions and improve their business processes. extracted association rules from a database contain important and confidential information that if published, the privacy of indivi...

We evaluate several heuristic solvers for correlation clustering, the NP-hard problem of partitioning a dataset given pairwise affinities between all points. We experiment on two practical tasks, document clustering and chat disentanglement, to which ILP does not scale. On these datasets, we show that the clustering objective often, but not always, correlates with external metrics, and that loc...

Partition of unity methods (PUMs) on graphs are simple and highly adaptive auxiliary tools for graph signal processing. Based a greedy-type metric clustering augmentation scheme, we show how partition can be generated in an efficient way graphs. We investigate PUMs combined with local basis function (GBF) approximation method order to obtain low-cost global interpolation or classification schem...