The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this paper, a new approach is presented for data stream clustering which is one of the popular subject in recent years. In this proposed approach, two distinct data stream algorithms are used. Proposed approach is based on integrating localized Linear Discriminant Analysis (LLDA) which is adopted from Linear Discriminant Analysis (LDA) for data stream to CEDAS which is used graph structure for...
In this paper we present a Linear Quadratic Gaussian (LQG) control design for large-dimensional network dynamic systems using the idea of network clustering. When networks have tens of thousands of nodes spread over a wide geographical span, the design of conventional output feedback controllers becomes numerically challenging, and their implementation requires a large number of communication links...
Millions of users create user profiles on social media. Changes made to an attribute in the user profiles on social media generate a huge volume of data representing a data stream. A framework has been proposed to analyze such data streams and cluster the attribute values related to each other.
Stream mining is a trending field of research in this digital age. With the increase in number of users of digital technologies, data is generating exponentially and so is the need to analyse it. This data is very huge in size and cannot be kept stored for a long time, so it must be processed as soon as possible to make space for newly arriving data & to achieve this different single scan algorithms...
The Nearest Neighbor Classification (NNC) has been widely used as classification method, due to its simplicity, classification efficiency and its ability to deal with different classification problems. Despite its good classification accuracy, the NNC suffers from many shortcomings on the execution time, noise sensitivity, high storage requirements and lack of interpretability. In this paper, we propose...
In many governments and private institutions, one of the major tasks is to select the best project proposals for allocating the fund. These funding organizations select the proposals by submitting them to the reviewers for review. Manual process is too difficult when the number of projects is more. The earlier models introduced ontology based Text mining methods to cluster the proposals of any language...
In data mining, clustering is a technique of regrouping similar objects with common proprieties in some clusters. K-means algorithm is the basic of clustering technique; it is the most widely used algorithm for diverse applications. This paper studies and analyses the efficiency of extending k-means results of a perfect sample set, to different sets by using Z-test proprieties, this is based on the...
The need of clustering the data has been increased day by day in various applications such as Intrusion Detection System, Image Recognition System, etc. Clustering is very much useful in splitting the huge unlabeled data itemset into meaningful groups using similarity metrics. But, at the same time, the cost of the clustering algorithm is computationally expensive for such high dimensional data. Therefore,...
Classical methods in combinational logic circuits design are not appropriate in practice for designing new circuits, which have different gates and high number of inputs. On the other hand, evolutionary designs are good alternatives for combinational logic circuit design, but have a common drawback namely, high randomness of their cross-over method. In order to overcome this drawback, a new genetic...
Clustering algorithm is one of the fundamental techniques in data mining, which plays a crucial role in various applications, such as pattern recognition, document retrieval, and computer vision. As so far, many effective algorithms have been proposed. Affinity Propagation is an algorithm requires no parameter indicating the number of clusters, which is the most distinguishing advantage compared to...
The efficiency of a WiFi system with dozens of base stations in relatively small physical area is determined by the optimal allocation of the radio channels to the mobile devices. Based on the increased penetration rate of the high traffic capable smartphones and accentuated usage of these devices in densely populated buildings intelligent hardware tools are needed to offer QoS level to the users...
Clustering is a classical unsupervised learning task, which is aimed to divide a data set into several groups with similar objects. Clustering problem has been studied for many years, and many excellent clustering algorithms have been proposed. In this paper, we propose a novel clustering method based on density, which is simple but effective. The primary idea of the proposed method is given as follows...
Based on the advantages of data smoothing, this paper presents a new collaborative filtering algorithm to solve the problem of data sparsity in recommender system. The key innovation of the algorithm consists of clustering and data smoothing. Clustering is used to find out the similarity between users. This paper adopts a new method to cluster users. Data smoothing is designed to solve the problem...
Cyber-physical systems usually consist of large numbers of spatially distributed autonomous sensors that monitor physical conditions and communicate with a main location. We consider the problem of positioning mobile storage facilities in a recycling network consisting of two types of nodes: collection points (neighborhood recycling bins) and mobile storage centers, and of finding the optimal number...
Shared Nearest Neighbor (SNN) Clustering is a well-established density based clustering algorithm, which can find clusters of different sizes, shapes, and densities. SNN has been widely adopted in numerous applications. As the size of dataset becomes extremely large nowadays, it is inefficient or even impossible for large-scale data to be stored and processed on a single machine. Therefore, the scalability...
Symmetric non-negative matrix factorization (SymNMF) has important applications in data analytics problems such as document clustering, community detection and image segmentation. In this paper, we propose a novel nonconvex variable splitting method for solving SymNMF. Different from the existing works, we prove that the algorithm converges to the set of Karush-Kuhn-Tucker (KKT) points of the nonconvex...
In this paper, we discuss proportional data clustering. It emerges In many applications such as document clustering and Image classification using bag of visual words approach. When deploying mixture models for clustering, there Is always a problem of initialization, and It Is common to initialize using K-means algorithm. In proposed work, we present K-means clustering approach using different distance...
Recently, due to the popularity of Web 2.0, considerable attention has been paid to the opinion leader discovery in social network. By identifying the opinion leaders, companies or governments can manipulate the selling or guiding public opinion, respectively. Additionally, detecting the influential comments is able to understand the source and trend of public opinion formation. However, mining opinion...
The criminal behavior is a disorderliness that is a combined result of social and economic aspects. The crime rate has expanded and the activities of criminals have broaden in last few decades due to better communication system and transport. Crimes cause terror and damage our community enormously in several means. In cities and towns the crime trends rises due to fast developmental activities and...
Wireless sensor networks WSNs are exceptional network comprise of network devices in large numbers and spatial circulation. They have different sensing capacity and participate to finish common task. It is seen in writing overview that density grid based clustering in WSN which has enhanced the execution of the WSNs by utilizing the gathering based information aggregation. In any case, no enhancement...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.