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检索条件"主题词=Clustering algorithm"

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A Cluster Based Method for Cell Segmentation 5

A Cluster Based Method for Cell Segmentation

引用

5th International Conference on Swarm Intelligence (ICSI)

作者： Wang, Fei Xu, Benlian Lu, Mingli Changshu Inst Technol Sch Elect & Automat Engn Changshu 215500 Peoples R China

ISBN: (纸本)9783319118970;9783319118963

In the field of cell biology, cell segmentation is an essential task in biomedical application. For this purpose, a cluster based method for cell segmentation is proposed. Firstly, an ant colony clustering algorithm is used to make pre-segmentation from which cell candidates are identified, then some noise spots are filtered with area feature, after that, a novel cluster algorithm is proposed to divide adhering cells into individuals. Finally, good results of segmentation can be achieved. Experimental result show that the method remains both the advantage of image segment of ant colony cluster and the ability of further process of pre-segmentation, which improves the performance of cell segmentation.

关键词： Ant colony algorithm clustering algorithm cell segmentation

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Feature Selection and Term Weighting

Feature Selection and Term Weighting

引用

IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (WI-IAT)

作者： Algarni, Abdulmohsen Tairan, Nasser King Khalid Univ Coll Comp Sci Abha 61411 Saudi Arabia

ISBN: (纸本)9781479941438

Term-based approaches can extract many features in text documents, but most include noise. Many popular text-mining techniques have been adapted to reduce noisy information from extracted features but still contains some noises features. However, the noise features are extracted from the same training documents that good features extracted from. Therefore, the main problem is that some training documents contain large a mount of noises data. If we can reduce the noises data in the training documents that would help to reduce noises in extracted features. Moreover, we believe that remove some of training documents (documents that contains noises data more than useful data) can help to improve the effectiveness of the classifier. Using the advantages of clustering method can help to reduce the affect of noises data. The main problem of clustering is defined to be that of finding groups of similar projects in the data. In this paper we introduce the methodology that using clustering algorithm to group training data before use it. Also we tested our theory that not all training documents are useful to train the classifier.

关键词： data mining data reduction feature extraction feature selection pattern classification pattern clustering text analysis classifier clustering algorithm group training data noise data reduction noisy information reduction term weighting approach text documents text-mining techniques training documents Feature extraction Frequency measurement Information retrieval Noise Text categorization Training Data mining Text mining Feature extraction Noise information retrieval text documents data mining Classifiers Text categorization frequency measurement text processing clustering algorithms text mining Training Functional training data reduction Pattern recognition

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Research on Topic Detection Strategy Based on Extension of Comments and HowNet Lexeme 10

Research on Topic Detection Strategy Based on Extension of C...

引用

10th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP)

作者： Liu, Yun Li, Xiaoxian Zhao, Bing Beijing Jiaotong Univ Dept Elect & Informat Engn Key Lab Commun & Informat Syst Beijing Municipal Commiss Educ Beijing Peoples R China

ISBN: (纸本)9781479953905

As a product of Web2.0, micro-blog is developing rapidly these years. More and more information spread on the micro-blog because of its high speed and convenience, social hotspots and news events included. As a result, discovering, extraction and analyzing information become researching hotspots. By studying micro-blog text and long text cluster, this article draws a conclusion that traditional cluster algorithms cannot be used to discover topics because of the length of text. Therefore, this article proposes a solution which is based on the extension of the comments and HowNet lexeme. By this method, the short text and diversified expression can be overcome. Finally, the simulation results show that the proposed algorithm would significantly diminish the bad effects which are the results of short-text and improve the accuracy of clustering results.

关键词： Microblogging short text clustering algorithm hot topics

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Cluster based Routing algorithm for Robustness 1

Cluster based Routing Algorithm for Robustness

引用

1st International Conference on Networks & Soft Computing (ICNSC)

作者： Ghive, Sharayu Patil, Kishor Kulkami, Shriram Markande, Shriram Dept Elect & Telecommun Pune Maharashtra India

ISBN: (纸本)9781479934867

Connectivity and robustness of ad-hoc cognitive radio network are the challenges faced by cognitive radio network due to moving nature of the channels, which are available for the communication to take place, among cognitive radio nodes. This challenge can be addressed by the concept of clustering, which groups the neighboring nodes of a cognitive radio network. It helps the node to switch to other channel i.e. coordinated channel switching. It also helps in better spectrum sensing and simplification of routing. But the connectivity between or within cluster could be lost due to sudden movement of the primary nodes. In the following work, two different distributed algorithms are discussed. A new algorithm RESS is proposed which helps to maintain the inter and intra cluster connectivity so that robustness can be strengthened between the nodes.

关键词： Cognitive Radio Network clustering algorithm Robust Routing mechanism ICC OCC

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Integrating Time Stamps into Discovering the Places of Interest

Integrating Time Stamps into Discovering the Places of Inter...

引用

10th International Conference on Intelligent Computing (ICIC)

作者： Zhou, Jun Zhao, Qinpei Li, Hongyu Tongji Univ Sch Software Engn Shanghai 200092 Peoples R China

ISBN: (纸本)9783319093390;9783319093383

With the employment of GPS embedded device, large numbers of data has been collected from location aware applications. It is interesting and challenging to discover meaningful information behind the data. Since the GPS data contains the time information, we take use of the time stamps of the GPS data in this paper for better discovering the places of interest. The collection usually contains large amounts of trajectories, where not every point has information. Therefore, a time stamp clustering algorithm is firstly proposed to reduce the size of raw data and also extract the points with more information. Different clustering algorithms are then conducted on the pre-processed data for extracting the places of interest. Finally, we compare the clustering algorithms on the GPS data by several external validity indexes.

关键词： Time Stamp clustering Trajectories Data Mining clustering algorithm External Validity

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A multi-objective cluster algorithm based on GEP

A multi-objective cluster algorithm based on GEP

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International Conference on Cloud Computing and Big Data (CCBD)

作者： Ni, Youcong Du, Xin Xie, Datong Ye, Peng Zhang, Kaihuo Wuhan Univ State Key Lab Software Engn Wuhan Peoples R China Fujian Normal Univ Fac Software Fuzhou Peoples R China Fujian Commercial Coll Dept Informat Management Engn Fuzhou Peoples R China Wuhan Text Univ Sch Math & Comp Wuhan Peoples R China

ISBN: (纸本)9781479966219

clustering is one of the main methods in data mining. Many clustering algorithms have been proposed so far. Among them, GEP-Cluster, a single-objective clustering algorithm, can automatically cluster with unknown clustering number. However, it is difficult for GEP-Cluster to find the high-quality solution in the limited search space. Aiming at the problems, a multi-objective clustering algorithm based on gene expression programming, MOGEP-Cluster, is proposed in this paper. To validate the effectiveness of MOGEP-Cluster, a set of experiments are performed on 5 benchmark datasets. The experimental results show that MOGEP-Cluster can find better solutions than GEP-Cluster.

关键词： clustering algorithm Gene expression programming multi-objective

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Face Recognition System with Automatic Training Samples Selection Using Self-organizing Map 56

Face Recognition System with Automatic Training Samples Sele...

引用

56th ELMAR International Symposium

作者： Jirka, Vojtech Feder, Matej Pavlovicova, Jarmila Oravec, Miloc Slovak Univ Technol Bratislava Fac Elect Engn & Informat Technol Bratislava 81219 Slovakia

ISBN: (纸本)9789531841993

The paper deals with evaluation of automatic training samples selection method based on self-organizing map (SOM) in face recognition systems. In earlier paper [1] we presented an approach for automatic training samples selection using various clustering algorithms with good results on the CMU PIE face database. We showed that with the use of SOM we can achieve a good training samples selection. In this paper we further evaluate this approach with the use of face recognition systems based on principal component analysis (PCA) and support vector machines (SVM). We compare the results with random (uncontrolled and controlled) training samples selection and we evaluate the recognition accuracy of each method.

关键词： biometry biometric recognition face recognition training process self-organizing map clustering algorithm PCA SVM

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Users Classification on Broadcast And Television System Based on Statistical Analysis System Software 4

Users Classification on Broadcast And Television System Base...

引用

4th International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC)

作者： Wan, Binbin Zhang, Taozheng Chai, Jianping Zhao, Min Zhang, Zhenlong Commun Univ China Dept Sci & Engn Beijing Peoples R China

ISBN: (纸本)9781479965755

In this paper, we take the guide data and the program data from the users of digital cable television programs as the experimental data to carry on our experiment. And we use the SAS software platform which is very efficient in data analyzing to realize the classification of our user to different parts. So we can achieve our destination of personal recommendation and precision advertizing.

关键词： SAS clustering algorithm user classification broadcast and television system data mining

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model of smart grid typical customer 6

model of smart grid typical customer

引用

China International Conference on Electricity Distribution (CICED)

作者： Gu, Cailian Liu, Li Sun, ZhiJun Lu, Xiaoxu Shenyang Inst Engn Dept Elect Engn Shenyang 110036 Liaoning Provin Peoples R China Jianping Power Co Jianping Peoples R China

ISBN: (纸本)9781479941254

the demand response(DR) is becoming the focus of the smart grid construction, as an important measure of DR. Time-of-use(TOU) power price are used more *** is necessary to play to the role of TOU to guide the power consumption according to the demand response,improve the efficiency of electric power resource allocation and the social benefit of power. In this paper, the problem about dividing the time period in the design of TOU is considered, based on traditional fuzzy membership function peak-valley time-period partitioning considering the typical power user demand response, clustering algorithm is adopted to establish the model. Example analysis shows that the proposed model established by the decision-making time division method, can fuse the executed demand response, scientific of time-period of TOU is also be improved.

关键词： DR Price of TOU clustering algorithm

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Virtualized Dynamic URL Assignment Web Crawling Model

Virtualized Dynamic URL Assignment Web Crawling Model

引用

International Conference on Advances in Engineering and Technology Research (ICAETR),

作者： Bhaginath, Wani Rohit Shingade, Sandip Shirole, Mahesh VJTI Dept CE & IT Bombay Maharashtra India

ISBN: (纸本)9781479963935

Web search engines are software systems that help to retrieve the information from the net by accepting the input in the form of query and providing the result as files, pages, images or information. These search engines heavily rely on the web crawlers that interact with millions of the web pages given a seed URL or a list of seed URLs. However, these crawlers demand a large amount of computing resources. The efficiency of web search engines depends upon the performance of the crawling processes. Despite the continuous improvement in the crawling processes still there is a need of improvement towards more efficient and low cost crawler. Most of the crawlers existing today have a centralized coordinator that brings the disadvantage of single point failure. Taking into consideration the shortfalls of the existing crawlers, this paper proposes an architecture of a distributed web crawler. The architecture addresses two issues of the existing web crawlers: the first is to create a low cost web crawler using the concept of virtualization of cloud computing. The second issue is a balanced load distribution based on dynamic assignment of the URLs. The first issue is solved using mutli-core machines where each multi-core processor is divided into number of virtual machines (VM) that can perform different crawling task in parallel. Second issue is addressed using a clustering algorithm that assigns requests to the machines as per the availability of the clusters thereby realizing the balance among components according to their real-time condition. This paper discusses a distributed architecture and details of the implementation of the proposed algorithm.

关键词： Crawler Dynamic assignment Seeds Virtualization clustering algorithm K-means clustering

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