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Wireless Sensor Networks (WSNs) consist of small nodes with sensing, computation, and wireless communication capabilities. Wireless Sensor Network (WSN) is a promising data mining solution for precision agriculture. Instrumented with wireless sensors, it will become available to monitor the plants for real time, such as air temperature, soil water content, and nutrition stress. This real time information...
The increasing availability of huge amounts of data pertaining to time and position of moving objects generated by different sources using a wide variety of technologies (e.g., RFID tags, GPS, GSM networks) leads to large spatial data collections. Mining such amounts of data is challenging, since the possibility to extract useful information from this peculiar kind of data is crucial in many application...
Summary form only given. Data streams are continuous flows of data. Examples of data streams include network traffic, sensor data, call center records and so on. Their sheer volume and speed pose a great challenge for the data mining community to mine them. Data streams demonstrate several unique properties: infinite length, concept-drift, concept-evolution, and feature-evolution. Concept-drift occurs...
The world has fundamentally changed as the Internet has become a universal means of communication. The Web is a huge virtual space where to express individual opinions and influence any aspect of life. Internet contains a wealth of data that can be mined to detect valuable opinions, with implications even in the political arena. Nowadays the Web sources are more accessible and valuable than ever before,...
This paper proposes a method of data stream clustering for stock data analysis. The method aims to retain shape and tend features during the clustering process. The experimental results show that the shape-based clustering over data streams can get the evolution accuracy of 95% with the reasonable parameters.
In functional magnetic resonance imaging (fMRI) data, activated voxels are usually very small in number and are embedded in a mass of inactive voxels. For clustering analysis, this situation generates an ill-balanced data problem among different classes of voxels. In this paper we propose a novel method to overcome the ill-balanced data problem, by reducing the number of voxels to be processed by...
The land use or land cover map depicts the physical coverage of the Earth's terrestrial surface according to its use (viz. vegetation, habitation, water body, bare soil, artificial structures etc.). Land use map generation from remotely sensed images is one of the challenging task of remote sensing technology. In this article, motivated from group forming behaviour of real ants, we have proposed two...
The K-Modes algorithm is one of the most popular clustering algorithms in dealing with categorical data. But the random selection of starting centers in this algorithm may lead to different clustering results and falling into local optima. In this paper we proposed a swarm-based K-Modes algorithm. The experimental results over two well known Soybean and Congressional voting categorical data sets show...
Entities of the real world require partition into groups based on even feature of each entity. Clusters are analyzed to make the groups homologous and well separated. Many algorithms have been developed to tackle clustering problems and are very much needed in our application area of gene expression profile analysis in bioinformatics. It is often difficult to group the data in the real world clearly...
This paper presents an incremental clustering algorithm based on DGC, a density-based algorithm we developed earlier. We experimented with real-life datasets and both methods perform satisfactorily. The methods have been compared with some well-known clustering algorithms and they perform well in terms of z-score cluster validity measure.
Clustering is used commonly in ad hoc networks for hierarchical routing. Size-bounded clustering has been proposed to restrict the maximum size of a cluster so that the routing load, and hence the energy drain, on the clusterhead is bounded. However, all the existing size-bounded clustering algorithms take the size bound as the number of nodes in the cluster. This approach may still result in high...
This paper presents a scheme, in which the collaborative efforts of limited number of sensor nodes provide the social guidance for localization process to be used for uniform distribution of the clusters, over the network area. Utilizing the information provided in the form of gbest value and the direction for position update as social guidance, the sensor nodes localize them iteratively adjusting...
In this study, we discuss recent advances in the theory and practice of exemplar-based clustering. In the context of clustering, exemplars are those representative objects in the data sets. A recently proposed approach called convex clustering with exemplar-based models, referred as (CCE), adopts a convex objective function with a global solution. Although the existing frame work of CCE is attractive,...
Plagiarism is the practice of claiming, or implying, original authorship or incorporating material from someone else's written or creative work, in whole or in part, into one's own without adequate acknowledgement. Unlike cases of forgery, in which the authenticity of the writing, document, or some other kind of object, itself is in question, plagiarism is concerned with the issue of false attribution...
Cluster analysis offers a suite of powerful unsupervised methods, commonly used as exploratory data analysis tools. Such tools can be proven especially useful when we face the situation of analyzing large data sets and want to get an intuitive insight at subtle correlations between instances of the data. In this work, we demonstrate that simple hierarchical clustering approaches (based on compositional...
The use of clustering as a data analysis tool has raised concerns about the violation of individual privacy. This paper proposes a data perturbation technique for privacy preservation in k-means clustering. Data objects that have been partitioned into clusters using k-means clustering are perturbed by performing geometric transformations on the clusters in such a way that the object membership of...
Several general-purpose algorithms and techniques have been developed for image segmentation. Since there is no general solution to the image segmentation problem, these techniques often have to be combined with domain knowledge in order to effectively solve an image segmentation problem for a problem domain. This paper presents a comparative study of the basic image segmentation techniques i.e. edge-based,...
The size of publicly indexable World Wide Web has probably surpassed 14.3 billion documents and as yet growth shows no sign of leveling off. As more information becomes available on the Web it is more difficult to provide effective search services for Internet users. Since, it is assumed that users do not always formulate search queries using the best terms. So, search engines invoke query expansion...
Clustering analysis has been an emerging research issue in data mining due its variety of applications. In the recent years, it has become an essential tool for gene expression analysis. Many clustering algorithms have been proposed so far. However, each algorithm has its own merits and demerits and can not work for all real situations. In this paper, we present a clustering algorithm that is inspired...
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