Clustering Method

A wireless network consisting of large number of small sensors with low-power transceivers. These devices rely on battery power so that; improvement in the energy of these networks becomes important. Wireless sensor network (WSN) require various power management protocols to reduce the power consumption. In this paper we study that different number of clustering algorithm leads to different network performance on the energy balancing, energy consumption and network lifetime. We propose a Multi-hop Hierarchical Stable Election Protocol (MHSEP) for wireless sensor networks to enhance the network lifetime and avoid the formation of energy holes. Classical clustering protocols assume that all the nodes in a system are equipped with the equal amount of energy and as a result, network cannot take full advantage of the presence of node heterogeneity in system. Our proposed protocol is a heterogeneous-aware protocol to increase the time-interval before the death of first node (we refer to a...

We discuss types of clustering problems where error information associated with the data to be clustered is readily available and where error-based clustering is likely to be superior to clustering methods that ignore error. We focus on clustering derived data (typically parameter ...

A multi-channel wireless EEG (electroencephalogram) acquisition and recording system is developed in this work. The system includes an EEG sensing and transmission unit and a digital processing circuit. The former is composed of pre-amplifiers, filters, and gain amplifiers. The kernel of the later digital processing circuit is a micro-controller unit (MCU, TI-MSP430), which is utilized to convert the EEG signals into digital signals and fulfill the digital filtering. By means of Bluetooth communication module, the digitized signals are sent to the back-end such as PC or PDA. Thus, the patient's EEG signal can be observed and stored without any long cables such that the analogue distortion caused by long distance transmission can be reduced significantly. Furthermore, an integrated classification method, consisting of non-linear energy operator (NLEO), autoregressive (AR) model, and bisecting k-means algorithm, is also proposed to perform EEG off-line clustering at the back-end. ...

This article addresses the problem of tracking moving objects using deformable models. A Kalman-based algorithm is presented, inspired by a new class of constrained clustering methods, proposed by Abrantes and Marques (1996) in the context of static shape estimation. A set of data centroids is tracked using intra-frame and inter-frame recursions. Centroids are computed as weighted sums of the edge points belonging to the object boundary. The use of centroids introduces competitive learning mechanisms in the tracking algorithm leading to improved robustness with respect to occlusion and contour sliding. Experimental results with traffic sequences are provided

Credit risk concentration is one of the leading topics in modern finance, as the bank regulation has made increasing use of external and internal credit ratings. Concentration risk in credit portfolios comes into being through an uneven distribution of bank loans to individual borrowers (single-name concentration) or in a hierarchical dimension such as in industry and services sectors and geographical regions (sectorial concentration).To measure single-name concentration risk the literature proposes specific concentration indexes such as the Herfindahl–Hirschman index, the Gini index or more general approaches to calculate the appropriate economic capital needed to cover the risk arising from the potential default of large borrowers.However, in our opinion, the Gini index and the Herfindahl–Hirschman index can be improved taking into account methodological and theoretical issues which are explained in this paper.We propose a new index to measure single-name credit concentration risk and we prove the properties of our contribution.Furthermore, considering the guidelines of Basel II, we describe how our index works on real financial data. Finally, we compare our index with the common procedures proposed in the literature on the basis of simulated and real data.

Customer Segmentation is an increasingly significant issue in today's competitive commercial area. Many literatures have reviewed the application of data mining technology in customer segmentation, and achieved sound effectives. But in the most cases, it is performed using customer data from a special point of view, rather than from systematical method considering all stages of CRM. This paper, with the aid of data mining tools, constructs a new customer segmentation method based on RFM, demographic and LTV data. The new customer segmentation method consists of two phases. Firstly, with K-means clustering, customers are clustered into different segments regarding their RFM. Secondly, using demographic data, each cluster again is partitioned into new clusters. Finally, using LTV, a profile for customer is created. The method has been applied to a dataset from Iranian bank, which resulted in some useful management measures and suggestions.

We herein introduce a new method of interpretable clustering that uses unsupervised binary trees. It is a three-stage procedure, the first stage of which entails a series of recursive binary splits to reduce the heterogeneity of the data within the new subsamples. During the second stage (pruning), consideration is given to whether adjacent nodes can be aggregated. Finally, during the third stage (joining), similar clusters are joined together, even if they do not share the same parent originally. Consistency results are obtained, and the procedure is used on simulated and real data sets.

While the majority of approaches to the characterization of complex networks has relied on measurements considering only the immediate neighborhood of each network node, valuable information about the network topological properties can be obtained by considering further neighborhoods. The current work considers the concept of virtual hierarchies established around each node and the respectively defined hierarchical node degree and clustering coefficient (introduced in cond-mat/0408076), complemented by new hierarchical measurements, in order to obtain a powerful set of topological features of complex networks. The interpretation of such measurements is discussed, including an analytical study of the hierarchical node degree for random networks, and the potential of the suggested measurements for the characterization of complex networks is illustrated with respect to simulations of random, scale-free and regular network models as well as real data (airports, proteins and word associations). The enhanced characterization of the connectivity provided by the set of hierarchical measurements also allows the use of agglomerative clustering methods in order to obtain taxonomies of relationships between nodes in a network, a possibility which is also illustrated in the current article.

Many scientific applications can benejit from eficient clustering algorithm of massively large high dimensional datasets. However most of the developed ,algorithms are impractical to use when the amount of data is very large. Given N objects each de3ned by an M-dimensional fea- ture vectol; any clustering technique for handling very large datasets in high dimensional space should run in time

In thi s research, we develop a segmentation methodology for reducing the computational time required for setting the (r, Q) inventory control polices in a large - scale multi echelon inventory system. The segmentation methodology uses a clustering algorithm to com press the inventory data into clusters of identical SKUs at each inventory control point and uses the reduced dataset