Density Clustering Method Research Articles

An Energy-Efficient Data Collection Method for Wireless Multimedia Sensor Networks

In wireless sensor networks (WSNs), some sensor nodes transmit their acquired data to a far distant base station directly or by linking several relay nodes. WSNs might not be able to operate if a sensor node completely consumes its battery power and does not perform its designated operation. Semiconductor technology drives sensor networks to collect video and audio data as well as environmental data. Therefore, energy is one of the most important resources in wireless multimedia sensor networks (WMSNs), and clustering is proposed to reduce energy consumption. When a mobile sink is used, a lot of energy is saved, and the lifetime of the network can be extended because the sensor nodes do not need to transmit multimedia data to a far distant base station. We propose an energy-efficient data collection method to extend the lifetime of networks that use a mobile sink. By using the neighborhood density clustering method and defining an optimal path for the mobile sink, the proposed method extends the life of the WMSN. Simulation results show that the proposed method increases the lifetime of WMSNs by up to 15 rounds, compared with LEACH. And data collection time is decreased owing to the predefined path for the mobile sink.

Determination Method of Nonlinear Membership Function Based on the Density Function of the Square Error

In this study, membership function is used to express the uncertainty in the petroleum drilling, then the fuzzy density clustering method based on density function of square error is put forward, which has avoided the defect of artificial clustering parameter and initial centroid, by using this fast algorithm, the clustering center, the number of clustering center and the parameters used for describing the nonlinear membership function can be obtained. In the simulation, the nonlinear membership function of change percentage of flow out rate EB_Da36 is determined, which has solved the problem that the membership function is hard to be defined.

The extended fuzzy C-means algorithm for hotspots in spatio-temporal GIS

In spatial analysis buffer impact areas are called hotspots and are determined by means of density clustering methods. In a previous work, we found these hotspots in the context of a Geographic Information System (GIS) by using the extended fuzzy C-means (EFCM). Here we show how the spatial distribution of the hotspots can evolve temporally and like applicational example, we present the spatial-temporal evolution in the period 2000–2006 of the fire point-events data of the Santa Fè district (NM) (downloaded from URL: www.fs.fed.us/r3/gis/sfe_gis.shtml).

Scalable Varied Density Clustering Algorithm for Large Datasets

Finding clusters in data is a challenging problem especially when the clusters are being of widely varied shapes, sizes, and densities. Herein a new scalable clustering technique which addresses all these issues is proposed. In data mining, the purpose of data clustering is to identify useful patterns in the underlying dataset. Within the last several years, many clustering algorithms have been proposed in this area of research. Among all these proposed methods, density clustering methods are the most important due to their high ability to detect arbitrary shaped clusters. Moreover these methods often show good noise-handling capabilities, where clusters are defined as regions of typical densities separated by low or no density regions. In this paper, we aim at enhancing the well-known algorithm DBSCAN, to make it scalable and able to discover clusters from uneven datasets in which clusters are regions of homogenous densities. We achieved the scalability of the proposed algorithm by using the k-means algorithm to get initial partition of the dataset, applying the enhanced DBSCAN on each partition, and then using a merging process to get the actual natural number of clusters in the underlying dataset. This means the proposed algorithm consists of three stages. Experimental results using synthetic datasets show that the proposed clustering algorithm is faster and more scalable than the enhanced DBSCAN counterpart.

Relational mountain (density) clustering method and web log analysis

The mountain clustering method and the subtractive clustering method are useful methods for finding cluster centers based on local density in object data. These methods have been extended to shell clustering. In this article, we propose a relational mountain clustering method (RMCM), which produces a set of (proto) typical objects as well as a crisp partition of the objects generating the relation, using a new concept that we call relational density. We exemplify RMCM by clustering several relational data sets that come from object data. Finally, RMCM is applied to web log analysis, where it produces useful user profiles from web log data. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 375–392, 2005.

Performance assessment of kernel density clustering for gene expression profile data

Kernel density smoothing techniques have been used in classification or supervised learning of gene expression profile (GEP) data, but their applications to clustering or unsupervised learning of those data have not been explored and assessed. Here we report a kernel density clustering method for analysing GEP data and compare its performance with the three most widely-used clustering methods: hierarchical clustering, K-means clustering, and multivariate mixture model-based clustering. Using several methods to measure agreement, between-cluster isolation, and withincluster coherence, such as the Adjusted Rand Index, the Pseudo F test, the r2 test, and the profile plot, we have assessed the effectiveness of kernel density clustering for recovering clusters, and its robustness against noise on clustering both simulated and real GEP data. Our results show that the kernel density clustering method has excellent performance in recovering clusters from simulated data and in grouping large real expression profile data sets into compact and well-isolated clusters, and that it is the most robust clustering method for analysing noisy expression profile data compared to the other three methods assessed.

Clustering High Dimensional Massive Scientific Datasets

Many scientific applications can benefit from an efficient 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 defined by an M-dimensional feature vector, any clustering technique for handling very large datasets in high dimensional space should run in time O(MN) at best, and O(MN log N) in the worst case, using no more than O(MN) storage, for it to be practical. We introduce a hybrid algorithm, called HyCeltyc, for clustering massively large high dimensional datasets in O(MN) time which is linear in the size of the data. HyCeltyc, which stands for Hybrid Cell Density Clustering method, combines a cell-density based algorithm with a hierarchical agglomerative method to identify clusters in linear time. The main steps of the algorithm involve sampling, dimensionality reduction, selection of significant features on which to cluster the data and a grid-based clustering algorihm that is linear in the data size.