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The Intuitionistic fuzzy clustering algorithms are sensitive to the initial value, easy to fall into local optimum and have slow convergence speed. To overcome these shortages, the particle swarm optimization (PSO) algorithm with powerful ability of global search and quick convergence rate is applied to Intuitionistic fuzzy clustering. Firstly, PSO is used to optimize the initial clustering centers...
We consider the dictionary learning problem in sparse representations based on an analysis model with noisy observations. A typical limitation associated with several existing analysis dictionary learning (ADL) algorithms, such as Analysis K-SVD, is their slow convergence due to the procedure used to pre-estimate the source signal from the noisy measurements when updating the dictionary atoms in each...
Traditional Top-N strategy for movie recommendation takes only users' ratings into account when mining users' needs or interests. But watching movie is a special behavior, in which users' interests should not just be represented by their ratings. We think that the decision to watch a movie also reflects the target users' needs, even though he or she may give it a low rating. In this paper, we introduce...
Cloud computing is a flexible computing model where resources are allocated and deal located dynamically. The dynamic nature of allocating resources provide scope for optimizing the resources utilization. However the key challenge is to optimize the resource utilization without impacting the applications. In this context we argue that knowing the usage (behavior) of applications is important and using...
In this paper we present two algorithms for performing sparse matrix-dense vector multiplication (known as SpMV operation). We show parallel (multicore) version of algorithm, which can be efficiently implemented on the contemporary multicore architectures. Next, we show distributed (so-called multinodal) version targeted at high performance clusters. Both versions are thoroughly tested using different...
Controlling a swarm of robots after deployment is difficult, due to the unpredictable and emergent behavior of swarm algorithms. Past work has focused on influencing the swarm via statically selected leaders—swarm members that the operator directly controls—that are pre-selected and remain leaders throughout the scenario execution. This paper investigates the use of dynamically selected leaders that...
The regularity of everyday tasks enables us to reuse existing solutions for task variations. For instance, most door-handles require the same basic skill (reach, grasp, turn, pull), but small adaptations of the basic skill are required to adapt to the variations that exist (e.g. levers vs. knobs). We introduce the algorithm “Simultaneous On-line Discovery and Improvement of Robotic Skills” (SODIRS)...
Modern Networked Critical Infrastructures (NCI), involving cyber and physical systems, are exposed to intelligent cyber attacks targeting the stable operation of these systems. In order to ensure anomaly awareness, the observed data can be used in accordance with data mining techniques to develop Intrusion Detection Systems (IDS) or Anomaly Detection Systems (ADS). There is an increase in the volume...
Agglutinative languages, such as Hungarian, use inflection to modify the meaning of words. Inflection is a string transformation which describe how can a word converted into its inflected form. The transformation can be described by a transformational string. The words can be classified by their transformational string, so inflection is considered as a classification. Linear separability of clusters...
This paper provides a comparative study of several enhanced versions of the fuzzy c-means clustering algorithm in an application of histogram-based image color reduction. A common preprocessing is performed before clustering, consisting of a preliminary color quantization, histogram extraction and selection of frequently occurring colors of the image. These selected colors will be clustered by tested...
This paper proposes a novel technique for clustering commits for understanding the intents of implementation. Such a classification of commits should be able to assist developers to understand commits related to particular requirements, for example, how and why has this function been implemented, or has this function suffered from any bugs? Our technique adopts a clustering algorithm on identifier...
A new fuzzy clustering algorithm (PPC) based on project pursuit model is presented in the paper. The main defect of the traditional clustering algorithm is to reduce dimension, while PPC don't need reduce dimension. Firstly, the text vector is normalized; Secondly, the project index function is constructed; Thirdly, the project function is optimized; Finally, the clustering result is acquired according...
This paper is researched on hybrid recommendation. It is mixed with user's similarity, Hamming distance, Cluster and Slope one to get a good prediction. This paper propose take into account Hamming distance to cluster .and then we take f(x)=x+b to predict the item rated in the cluster. At last, This paper verified the model through experiments and carried out the proposed recommendation model drawn...
Visualizing information extracted from text is helpful for intuitively understanding the information. Extracting and visualizing personal relationships from text is one of the promising applications of this approach. Existing methods usually estimate personal relationships from direct co-occurrences of personal names that appear in a text. In our previous work, we proposed a method for extracting...
Short text messages, especially mobile SMSs contain not only pure textual strings but also contain numeric values. Existing systems discard and filter out these numeric values. In our research, a new approach has been developed which makes usage of numeric values for feature extraction in the process of clustering. We are proposing an algorithm that uses n-gram approach to retrieve the pre-strings...
Botnets have become one of the major tools used by attackers to perform various malicious activities on the Internet, such as launching distributed denial of service attacks, sending spam, leaking personal information, and so on. In this paper, we present BotCatch, a behavior-based botnet detection system that considers multiple coordinated group activities in the monitored network to identify bot-infected...
Diversification of results in web search engines is a very attractive area for researchers now a days. Information retrieval techniques mainly focus on the relevance of the documents retrieved but these techniques often fail to satisfy each user. In this work, we present a coverage based diversification using post retrieval clustering. We model clusters corresponding to the query based on the features...
Traditional k-means algorithm has been used successfully to various problems but its application is restricted to small datasets. Online websites like twitter have large amount of data that has to be handled properly. So, there is a need of a platform that can perform faster data clustering which leds to the development of Mahout/Hadoop. Mahout is machine learning library approach to parallel clustering...
Recently peer to peer botnets have become one of the formidable threats to the Internet. Therefore P2P botnets are considered as a serious challenges to botnet detection researches. In recent years many methods are proposed to detect P2P botnets based on similarity or failures analysis of flow network, however, none of these methods alone are not sufficient to detect new P2P botnets. In this paper...
An increasing interest has been recently devoted to clustering short documents. Short documents don't contain enough text to compute similarities accurately by implementing the most widely used technique called Vector Space Model (VSM). Adding semantics to short documents clustering is one efficient way to solve this problem. However, real life collections are often composed of very short or long...
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