Automatic grouping (clustering) involves dividing a set of objects into subsets (groups) so that the from one subset are more similar to each other than according some criterion. Kohonen neural networks class artificial networks, main element which is layer adaptive linear adders, operating on principle “winner takes all”. One advantages their ability online clustering. Greedy agglomerative pro...

We study the online clustering problem where data items arrive in an online fashion. The algorithm maintains a clustering of data items into similarity classes. Upon arrival of v, the relation between v and previously arrived items is revealed, so that for each u we are told whether v is similar to u. The algorithm can create a new cluster for v and merge existing clusters. When the objective i...

We propose a method for the classification of more than two classes, from high-dimensional features. Our approach is to build a binary decision tree in a top-down manner, using the optimal margin classifier at each split. We implement an exact greedy algorithm for this task, and compare its performance to less greedy procedures based on clustering of the matrix of pairwise margins,. We compare ...

In this work, the capacitated location-routing problem with simultaneous pickup and delivery (CLRP-SPD) is considered. This problem is a more realistic case of the capacitated location-routing problem (CLRP) and belongs to the reverse logistics of the supply chain. The problem has many real-life applications of which some have been addressed in the literature such as management of liquid petrol...

K-means clustering technique works as a greedy algorithm for partition the n-samples into k-clusters so as to reduce the sum of the squared distances to the centroids. A very familiar task in data analysis is that of grouping a set of objects into subsets such that all elements within a group are more related among them than they are to the others. K-means clustering is a method of grouping ite...

Clustering is a fundamental problem in computer science with applications ranging from biology to information retrieval and data compression. In a clustering problem, a set of objects, usually represented as points in a high-dimensional space R, is to be partitioned such that objects in the same group share similar properties. The k-means method is a traditional clustering algorithm, originally...

BACKGROUND The operational taxonomic unit (OTU) is widely used in microbial ecology. Reproducibility in microbial ecology research depends on the reliability of OTU-based 16S ribosomal subunit RNA (rRNA) analyses. RESULTS Here, we report that many hierarchical and greedy clustering methods produce unstable OTUs, with membership that depends on the number of sequences clustered. If OTUs are re...

In this article, we propose a novel algorithm to obtain solution the clustering problem with an additional constraint of connectivity. This is achieved by suitably modifying K-Means include connectivity constraints. The modified involves repeated application watershed transform, and hence referred as iterated watersheds. Detailed analysis performed using toy examples. Iterated watersheds compar...