However evidence is growing for the presence of well evolved rich clusters and cluster galaxies at high redshift (above z=0.5-1; Dickinson 1997, Mushotzky and Loewenstein 1997), which sets a key constraint on galaxy evolution models and on structure formation models as well. After IRAS, which proved that starbursts emitted mostly in the infrared (Sanders & Mirabel 1996), ISOCAM is finding a str...

The interest in reconstructing phylogenetic relationships from data on structural similarity of metabolic pathways is growing. The similarity notions and the techniques involved in this reconstruction are assessed by building phylogenetic relationships for model sets of organisms from the similarity measures of the same metabolic pathway for all of them, and then the phylogenetic trees obtained...

The clustering of the Web users based on their access patterns is studied. Access patterns of the Web users are extracted from Web servers' log les, and then organized into sessions which represent episodes of interaction between Web users and the Web server. Using attributed-oriented induction, the sessions are then generalized according to the page hierarchy which organizes pages according to...

The Rclusterpp package provides alternative implementations for common geometric hierarchical clustering algorithms, e.g., average link clustering, that are optimized for large numbers of observations and efficient execution on modern multicore processors. Rclusterpp can be used directly from R as a replacement for stats::hclust, or as a linkable C++ library.

Network monitoring applications are used to monitor network traffic flows. Clustering techniques are used to extract network traffic patterns. Anomaly detection schemes are used to detect network attacks. Hierarchical and partitional clustering schemes are used to analyze network traffic data values. The hierarchical data analysis uses the structure and data values for the clustering process. T...

Agglomerative hierarchical clustering algorithm used with top down approach. It implement with multiple attributes. In multiple attributes frequency calculation is allocated. Memory requirements are less in this process. Hierarchical clustering produce accurate result than any other algorithm. This is very less time consuming process.

We summarize our work with ADHIC (Approximate Divisive HIerarchical Clusterer), a lightweight, online, divisive hierarchical clustering algorithm tailored to the domain of network traffic clustering. We then briefly describe our implementation of ADHIC, NetADHICT, which serves as a tool to system administrators. The key innovation is that it can identify and present a hierarchical decomposition...

A new two layer hierarchical routing protocol called Cluster Based Hierarchical Routing Protocol (CBHRP) is proposed in this paper. It is an extension of LEACH routing protocol. We introduce cluster head-set idea for cluster-based routing where several clusters are formed with the deployed sensors to collect information from target field. On rotation basis, a head-set member receives data from ...

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...

In this paper we present a method to detect natural groups in a data set, based on hierarchical clustering. A measure of the meaningfulness of clusters, derived from a background model assuming no class structure in the data, provides a way to compare clusters, and leads to a cluster validity criterion. This criterion is applied to every cluster in the nested structure. While all clusters passi...