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Object Recognition and clustering are major techniques in Pattern Recognition, Computer Vision, Artificial Intelligence and Robotics. Conventionally these techniques are implemented in Visual-Feature based methods and Cosine Similarity method or Vector Space method which uses semantic similarity among the objects to solve these kinds of problem, but this method has two problems synonymy and polysemy...
For the purposes of efficient network resource management and QoS (Quality of Service) support of different video services, this paper proposes a fine grained classification scheme for Internet video traffics based on hierarchical clustering. We study a number of QoS and network resource requirements related statistical features of some typical video applications and validate their effectiveness in...
This paper proposed an automatic clustering algorithm based on entropy for discovering the interest pattern over users' web log. We introduced the information entropy on the basis of clustering algorithm. Compared with traditional clustering algorithms, our method does not require any parameters specified by the end user. Meanwhile, it can discover the clusters in arbitrary shape and size. Experimental...
The user enters any query to find desired information. To discover number of user search goals and representing each goal with some keyword, we first infer user search goals for a query by clustering feedback sessions. For that, we use a concept of pseudo document, which is the revised version of feedback session. Then the user search goals are determined by clustering the pseudo documents and it...
Wide range of researchers have explored and criticized the in-network caching performance for a set of algorithms, recently. While there are quite a few caching architecture proposals for Information-centric networks (ICN) to increase the effectiveness of it, there has also been criticisms against it stating that clever ideas do not improve the efficiency latency-wise. On the other hand, the default...
There are few Chinese dish recommendation algorithms due to the variety of Chinese dishes. It could be impossible to find one's most liked dishes in a restaurant through the name or the ingredients of a dish. The algorithm in this paper uses the user's ordering history to quantify one's taste by k-means clustering method and determines the number of user's favorite tastes by the BWP index. With the...
Internet evolution follows the customer needs, each algorithm, protocol, architecture, equipment, functionality succeeded when the users perceived a real benefit in using it. Taking into account the impact of customer experience when designing promising and future proof technologies is essential. In this paper we investigate how it is possible to control network resources on the base of the Quality...
The ever-increasing user generated digital data available through the Internet has become an important source of information for individuals, organizations and government agencies. And yet, for users to fully discover and utilize those information remains a complex tasks. Existing popular information access models based on keyword and/or facet searches become less effective in providing access to...
In this world of emerging technologies, online frauds are rapidly increasing with the increasing popularity of online shopping era. It has been identified in the past research that the shilling is main cause behind online auction frauds. Several researchers have proposed various methods to counter the possibility of shilling in online auction. In this paper we have proposed a mechanism which uses...
Path-based graph algorithms are key building blocks for several link prediction and spatial mining applications. As the sizes of social, transport and communication networks expand, performing scalable traversal algorithms like SSSP are critical. While there is heightened interest in vertex-centric platforms for scalable graph analysis, there is limited literature on understanding the behavior of...
In past days, although we have focused on to collect required data, we can get required information since many data are storage and disclosed. Therefore, it has become a new task to search efficiently required information. Nowadays, the search engine such as Google, Bing and Baidu help us to search information in the internet. However, enormous number of search results is listed. In some cases, the...
In content oriented application, such as P2P, a user does not care about where content is obtained and a content file can be downloaded from anywhere it is obtained. In some P2P systems, e.g. BitTorrent, a content file is divided into small parts, chunks, and each chunk is downloaded from anywhere. In this many-to-one communication style, one TCP session is set up for each pair of a sender and a receiver...
The online retail industry is one of the world's largest and fastest growing industries having huge amount of online sales data. This sales data includes information about customer buying history, goods or services offered for the customers. Hidden relationships in sales data can be discovered from the application of data mining techniques. Data mining is an inter disciplinary promising field that...
The ability to effectively organize textual information is a big challenge in intelligent text processing. With the increase in the amount of textual data being generated, this task is becoming more and more essential. In this paper we present an unsupervised computer-aided tool for automatically building classification schemes and taxonomies for enhancing the process of automated text classification...
The rising of the modern Internet brought with it heap opportunities for attackers to gain illegal benefit from spreading spam mail. Spam is irrelevant or inappropriate messages sent on the Internet to a large number of recipients. Many researchers use a large number of classification method in machine learning to filter spam messages. But, there is still limited research which evaluate the use of...
Network traffic classification is important for QoS, Network management and security monitoring. Current method for traffic classification such as port based or payload based suffered many problems. Newly emerged application uses encryption and dynamic port numbers to avoid detection. So we use unsupervised machine learning approach to classify the network traffic. In this paper unsupervised K-means...
The spread of "rumours" in Online Social Networks (OSNs) has grown at an alarming rate. Consequently, there is an increasing need to improve understanding of the social and technological processes behind this trend. The first step in detecting rumours is to identify and extract memes, a unit of information that can be spread from person to person in OSNs. This paper proposes four similarity...
In this paper, we have proposed a novel K-means algorithm with modified Cosine Distance Measure for clustering of large datasets like Wikipedia latest articles and Reuters dataset. We are customizing Cosine Distance Measure for computing similarity between objects for improving cluster quality. Our method will calculate the similarity between objects by Cosine Distance Measure and then try to bring...
Hierarchical Cluster Labeling helps users to quickly understand and analyze hierarchical clusters. This may be used to enhance search engine results or interactive browsing like it is being used in the Blog Intelligence application. The hierarchical organization of data helps to represent different levels of detail. Hierarchical clustering may be quite common, but there are few good solutions for...
With the continuous development of the Internet technology, nowadays personalized service and recommendation technology have been paid more attentions. The paper aims at accurate user classification for tag application systems and proposes the feasible solution which can mine users' intention in reviews and extend the tag semantics by open knowledge platform. Experiments validate the proposed solution...
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