Dynamic clustering problems can be solved by finding several clustering solutions with different number of clusters, and by choosing the one that minimizes a given evaluation function. This kind of brute force approach is general but not very efficient. We propose a new dynamic local search that solves the number and location of the clusters jointly. The algorithm uses a set of basic operations...

Wireless sensor networks have the problem of lifetime and scalability. To increase lifetime and scalability it is necessary to have control over topology of the network even when dynamically changes are observed. Dynamic clustering with adaptive feature is the best way to achieve the above. In this paper we propose a dynamic multi-level hierarchal clustering (DMH) approach for sensor networks. ...

Abstract. Maximizing the quality index modularity has become one of the primary methods for identifying the clustering structure within a graph. As contemporary networks are not static but evolve over time, traditional static approaches can be inappropriate for specific tasks. In this work we pioneer the NP-hard problem of online dynamic modularity maximization. We develop scalable dynamization...

Radio fingerprinting is the process which identifies a cellular phone / other radio transmitter by the unique "fingerprint" which characterizes the signal transmission. Location fingerprinting determines receiver position with the help of database of RSS measurements from different sources .Location fingerprinting a positioning method that explores existing infrastructures e.g. cellular network...

In this paper, an on-line, dynamic clustering method — ECM, Evolving Clustering Method, is proposed. The ECM is usually used for on-line systems, in which it performs an one-pass, maximum distance-based clustering process without any optimisation. The ECM can also be applied to off-line problems where a constrained minimisation is introduced.

We evaluated phylogenetic clustering of bacterial and archaeal communities from redox-dynamic subtropical forest soils that were defined by 16S rRNA and rRNA gene sequences. We observed significant clustering for the RNA-based communities but not the DNA-based communities, as well as increasing clustering over time of the highly active taxa detected by only rRNA.

This paper presents a novel growing self-organizing algorithm for dynamic clustering. Controlling a signal counter of each cell, the network can grow. Also, if there exists undesired cell for the clustering, the cell can be deleted virtually. Our algorithm can reinforce the clustering function and the learned network can adapt flexibly to time-variant input space.

With this project, a few dynamic file clustering algorithms, namely: Term consistency based Greatest Resemblance Doc Clustering (TMARDC), Correlated Concept primarily based MAximum Resemblance Document Clustering (CCMARDC) and Correlated Notion based Quickly Incremental Clustering Criteria (CCFICA) usually are proposed. From the aforementioned three suggested algorithms this TMARDC algorithm wi...

The Internet uses hierarchical networking, for scalability and manageability reasons. However the main ad hoc routing solutions (OLSR [1], AODV [6], DSR [8], TBRPF [7]) only provide flat networking, and generally suffer important scalability issues. This paper therefore introduces a simple mechanism providing dynamic clustering with OLSR, one of the MANET routing solutions, chosen for its easy ...