monitoring of road traffic noise is becoming an important issue in modern cities due to the spreading of noise pollution and the extension of monitored areas. thus, the stratified spatial sampling is frequently applied to reduce the costs and provide adequate accuracy in order to obtain reliable noise maps. the definition of the strata in the sampling may refer to the legislative classification...

Hierarchical methods are well known clustering technique that can be potentially very useful for various data mining tasks. A hierarchical clustering scheme produces a sequence of clusterings in which each clustering is nested into the next clustering in the sequence. Since hierarchical clustering is a greedy search algorithm based on a local search, the merging decision made early in the agglo...

Hierarchical methods are well known clustering technique that can be potentially very useful for various data mining tasks. A hierarchical clustering scheme produces a sequence of clusterings in which each clustering is nested into the next clustering in the sequence. Since hierarchical clustering is a greedy search algorithm based on a local search, the merging decision made early in the agglo...

In this paper we address the problem of building object class representations based on local features and fast matching in a large database. We propose an efficient algorithm for hierarchical agglomerative clustering. We examine different agglomerative and partitional clustering strategies and compare the quality of obtained clusters. Our combination of partitional-agglomerative clustering give...

Cluster analysis is a crucial technique in unsupervised machine learning, pattern recognition, and data analysis. However, current clustering algorithms suffer from the need for manual determination of parameter values, low accuracy, inconsistent performance concerning size structure. To address these challenges, novel algorithm called fully automated density-based method (FADBC) proposed. The ...

We propose an agglomerative hierarchical kernel spectral clustering (AH-KSC) model for large scale complex networks. The kernel spectral clustering (KSC) method uses a primal-dual framework to build a model on a subgraph of the network. We exploit the structure of the projections in the eigenspace to automatically identify a set of distance thresholds. These thresholds lead to the different lev...

The rapid development of microarray technologies enabled the monitoring of expression levels of thousands of genes simultaneously. Microarray technology has great potential for creating an enormous amount of data in a short time, and now becomes a new tool for studying such broad problems as classification of tumors in biology and medical science. Many statistical methods are available for anal...

Hierarchical Clustering is a procedure of cluster analysis which aims to construct a hierarchy of clusters. There are two kinds of hierarchical clustering i.e. Agglomerative, which is a bottom – up approach, where all the observations start in its own cluster, and pairs of clusters are merged moving up the hierarchy, and the other one is divisive, which is a top down approach, where each observ...