Browsing 08 Information and Computing Sciences by Title

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Browsing 08 Information and Computing Sciences by Title

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  • Lin, Li; Cao, Longbing (Inderscience Publishers, 2008)
    Stock trading plays an important role for supporting profitable stock investment. In particular, more and more data mining-based technical trading rules have been developed and used in stock trading systems to assist ...
  • Feng, Ling; Dillon, Tharam (Springer-Verlag Berlin, 2005)
    XML-enabled association rule framework [FDWC03] extends the notion of associated items to XML fragments to present associations among trees rather than simple-structured items of atomic values. They are more flexible and ...
  • Zhang, Jilian; Zhu, Xiaofeng; Li, Xianxian; Zhang, Shichao (Springer, 2013)
    Recommender systems can predict individual user?s preference (individual rating) on items by examining similar items? popularity or similar users? taste. However, these systems cannot tell item?s long-term popularity. ...
  • Lo, D; Li, Jinyan; Wong, Ls; Khoo, Sc (IEEE Computer Soc, 2011)
    Billions of dollars are spent annually on software-related cost. It is estimated that up to 45 percent of software cost is due to the difficulty in understanding existing systems when performing maintenance tasks (i.e., ...
  • Wang, Chao; Lu, Jie; Zhang, Guangquan (Elsevier, 2007)
    Web content mining aims to discover useful information and generate desired knowledge from a large amount of web pages. Key information, such as distinctive menu items, navigation indicators, which is embedded in web pages, ...
  • Mao, Gojun; Wu, Xindong; Zhu, Xingquan; Chen, G; Liu, Chunnian (Sage Publications Ltd, 2007)
    Frequent pattern mining from data streams is an active research topic in data mining. Existing research efforts often rely on a two-phase framework to discover frequent patterns: (1) using internal data structures to store ...
  • Wang, Peng; Zhang, Peng; Guo, Li (SIAM, 2012)
    Data stream classification has drawn increasing attention from the data mining community in recent years, where a large number of stream classification models were proposed. However, most existing models were merely focused ...
  • Zhang, Shichao; Zaki, Mj (Springer, 2006)
    Many large organizations process data from multiple data sources, such as the different branches of an interstate or international company. Also theWeb has emerged as a large, distributed data repository consisting of a ...
  • Liu, Chunyang; Chen, Ling; Zhang, Chengqi (SIAM / Omnipress, 2013)
    Probabilistic frequent pattern mining over uncertain data has received a great deal of attention recently due to the wide applications of uncertain data. Similar to its counterpart in deterministic databases, however, ...
  • Chen, G; Wu, Xindong; Zhu, Xingquan (Springer, 2008)
    (I)n this paper, we deal with mining sequential patterns in multiple time sequences. Building on a state-of-the-art sequential pattern mining algorithm PrefixSpan for mining transaction databases, we propose MILE ((MI) ...
  • Qin, Zhenxing; Liu, Li; Zhang, Shichao (Springer, 2004)
    As the Web has become an important channel of information floods, users have had difficulty on identifying what they really want from huge amounts of rubbish-like information provided by the Web search engines when utilizing ...
  • Zhang, Chengqi; Yan, Xiaowei; Zhang, Shichao; Kennedy, Paul (CSREA Press, 2002)
  • Xuan, Junyu; Luo, Xiangfeng; Lu, Jie (IEEE, 2013)
    On the web, there are numerous websites publishing web pages to cover the events occurring in society. The web events data satisfies the well-accepted attributes of big data: Volume, Velocity, Variety and Value. As a great ...
  • Wu, Xindong; Zhu, Xingquan (IEEE-Inst Electrical Electronics Engineers Inc, 2008)
    Real-world data mining deals with noisy information sources where data collection inaccuracy, device limitations, data transmission and discretization errors, or man-made perturbations frequently result in imprecise or ...
  • Zhang, Shichao; Jin, Zhi; Zhu, Xiaofeng; Zhang, Jilian (Springer, 2009)
    Many missing data analysis techniques are of single-imputation. However, single-imputation cannot provide valid standard errors and confidence intervals, since it ignores the uncertainty implicit in the fact that the imputed ...
  • Zhang, Shichao; Qin, Zhenxing; Ling, Charles; Sheng, Shengli (IEEE Computer Soc, 2005)
    Many real-world data sets for machine learning and data mining contain missing values and much previous research regards it as a problem and attempts to impute missing values before training and testing. In this paper, we ...
  • Qin, Zhenxing; Zhang, Shichao; Zhang, Chengqi (IOS Press, 2006)
  • Zhang, Shichao; Zhang, Jilian; Zhu, Xiaofeng; Qin, Yongsong; Zhang, Chengqi (Springer, 2008)
    We propose an efficient nonparametric missing value imputation method based on clustering, called CMI (Clustering-based Missing value Imputation), for dealing with missing values in target attributes. In our approach, we ...
  • Gulrez, Tauseef; Al-Hmouz, Rami; Al-Jumaily, Adel Ali (Ahmad Faris Ismail, 2005)
  • Al-Sharawneh, Jebrin; Williams, Mary-Anne; Wang, Xun; Goldbaum, David (The International Academy, Research and Industry Association (IARIA), 2011)
    In the Service Web, a huge number of Web services compete to offer similar functionalities from distributed locations. Since no Web service is risk free, this paper aims to mitigate the risk in service selection using ...