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Outlier detection is a hot topic of data mining. After analyzing current detection technologies, a detection method of outlier based on clustering analysis is proposed, in which an effective sample is screened out from original data. According to agglomerative of hierarchical clustering, credible sample set is found. Then mathematical expectation and standard deviation are obtained by credible sample...
There is a great deal of general information and behavior information of customers stored in clickstream data warehouse, so it has lots of data sources. Data extraction technologies such as traditional web server logs and packet sniffer have many inadequacies in the extraction efficiency and accuracy. According to the actual needs of OLAP and DM, this dissertation proposes a hybrid data extraction...
Outlier detection is a hot topic of data mining. After studying the existing classical algorithms of detecting outlier, this paper proposes an outlier mining algorithm based on confidence interval, and makes a new definition for outlier. The method combines mathematical statistics and density-based clustering algorithm. It clustering firstly with DBSCAN algorithm, obtains credible sample and suspicious...
Outlier detection is a hot topic of data mining. After studying the existing classical algorithms of detecting outliers, this paper proposes an outlier mining algorithm based on probability, and makes a new definition for outlier. It clusterings firstly with density-based algorithm, and determines suspicious outlier. Then, outlier will be detected according to probability. The experiment results on...
Based on post-WTO sample data, this paper investigates the interaction among commodity price index, PPI and CPI after China has entered World Trade Organization (WTO) and fulfilled its promise of opening certain sectors. Granger casualty test is employed to conduct this research and the test result shows that the most international commodity price index, Rogers International Commodity Index (RICI),...
Cluster analysis is an important branch and effective tool for data mining. And cluster analysis has long played an important role in a wide variety of fields. The traditional Chinese medicine (TCM) diagnosis has been a challenging research problem. This paper introduces an original study between cluster analysis and the symptoms and signs of in patients with UA. We report a cluster analysis to evade...
Individual heterogeneity is important information but has not yet been considered in most researches on software outsourcing. This paper makes explicit of individual heterogeneity by using linear mixed model (LMM) based on dataset of Japan software outsourcing. Estimates prove the existence of individual heterogeneity. We also find well-defined and easy-to-monitor software are preferred in Japanese...
Outlier detection is a hot topic of data mining. After studying the existing classical algorithm of detecting outliers, this paper proposes a new algorithm for outlier detection based on offset, and makes a new definition for outlier. This detection algorithm is a method based on clustering analysis. It includes cluster modeling and data detection. Also, the clustering result obtained together with...
Many years of information progress makes organization or institute has different kinds of OLTP systems which are running under heterogeneous environment. And these OLTP systems accumulate lots of historical data. In order to provide effective global information for decision makers, global OLTP plays a crucial role. But, traditional method demands spending expensive to integrate the data in distributed...
The Intelligent Accountability Middleware Architecture (Llama) project supports dependable service-oriented architecture (SOA) monitoring, runtime diagnosis, and reconfiguration. At its core, Llama implements an accountability service bus that users can install on existing service-deployment infrastructures. It collects and monitors service execution data from a key subset of services; enables Llama...
Service-oriented architecture (SOA) provides a flexible paradigm to dynamically compose service processes from individual services. The flexibility, on the other hand, makes it necessary to monitor and manage service behaviors at runtime for performance assurance. One solution is to deploy software monitoring agents. In this paper, we present an approach to consider agent cost at process composition...
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