The Experts below are selected from a list of 31179 Experts worldwide ranked by ideXlab platform

Ahmed K Elmagarmid - One of the best experts on this subject based on the ideXlab platform.

  • multiple and partial periodicity mining in time series databases
    European Conference on Artificial Intelligence, 2002
    Co-Authors: Christos Berberidis, Walid G Aref, Mikhail J Atallah, Ioannis Vlahavas, Ahmed K Elmagarmid
    Abstract:

    Periodicity search in time series is a problem that has been investigated by mathematicians in various areas, such as statistics, economics, and digital signal processing. For large databases of time series data, scalability becomes an issue that traditional techniques fail to address. In existing time series mining algorithms for detecting periodic patterns, the period length is user-specified. This is a drawback especially for datasets where no period length is known in advance. We propose an algorithm that extracts a set of candidate periods featured in a time series that satisfy a Minimum Confidence threshold, by utilizing the autocorrelation function and FFT as a filter. We provide some mathematical background as well as experimental results.

  • ECAI - Multiple and Partial periodicity mining in time series databases
    2002
    Co-Authors: Christos Berberidis, Walid G Aref, Mikhail J Atallah, Ioannis Vlahavas, Ahmed K Elmagarmid
    Abstract:

    Periodicity search in time series is a problem that has been investigated by mathematicians in various areas, such as statistics, economics, and digital signal processing. For large databases of time series data, scalability becomes an issue that traditional techniques fail to address. In existing time series mining algorithms for detecting periodic patterns, the period length is user-specified. This is a drawback especially for datasets where no period length is known in advance. We propose an algorithm that extracts a set of candidate periods featured in a time series that satisfy a Minimum Confidence threshold, by utilizing the autocorrelation function and FFT as a filter. We provide some mathematical background as well as experimental results.

Lou Xiaohong - One of the best experts on this subject based on the ideXlab platform.

  • An Association Rule Mining Algorithm Based on Multiple Supports
    Computer Engineering, 2001
    Co-Authors: Lou Xiaohong
    Abstract:

    Association rule mining is an important model in data mining. Its mining algorithms discover all item associations (or rule) in the data that satisfy the user-specified Minimum support (minsup) and Minimum Confidence (minconf) constraints. In order to avoid rare item problem, Liu Bing proposed a model with multiple Minimum supports. This paper proposes a new algorithm based on the multiple Minimum model.

Christos Berberidis - One of the best experts on this subject based on the ideXlab platform.

  • multiple and partial periodicity mining in time series databases
    European Conference on Artificial Intelligence, 2002
    Co-Authors: Christos Berberidis, Walid G Aref, Mikhail J Atallah, Ioannis Vlahavas, Ahmed K Elmagarmid
    Abstract:

    Periodicity search in time series is a problem that has been investigated by mathematicians in various areas, such as statistics, economics, and digital signal processing. For large databases of time series data, scalability becomes an issue that traditional techniques fail to address. In existing time series mining algorithms for detecting periodic patterns, the period length is user-specified. This is a drawback especially for datasets where no period length is known in advance. We propose an algorithm that extracts a set of candidate periods featured in a time series that satisfy a Minimum Confidence threshold, by utilizing the autocorrelation function and FFT as a filter. We provide some mathematical background as well as experimental results.

  • ECAI - Multiple and Partial periodicity mining in time series databases
    2002
    Co-Authors: Christos Berberidis, Walid G Aref, Mikhail J Atallah, Ioannis Vlahavas, Ahmed K Elmagarmid
    Abstract:

    Periodicity search in time series is a problem that has been investigated by mathematicians in various areas, such as statistics, economics, and digital signal processing. For large databases of time series data, scalability becomes an issue that traditional techniques fail to address. In existing time series mining algorithms for detecting periodic patterns, the period length is user-specified. This is a drawback especially for datasets where no period length is known in advance. We propose an algorithm that extracts a set of candidate periods featured in a time series that satisfy a Minimum Confidence threshold, by utilizing the autocorrelation function and FFT as a filter. We provide some mathematical background as well as experimental results.

Dimitrios Gunopulos - One of the best experts on this subject based on the ideXlab platform.

  • Constraint-Based Rule Mining in Large, Dense Databases
    Data Mining and Knowledge Discovery, 2000
    Co-Authors: Roberto J Bayardo, Rakesh Agrawal, Dimitrios Gunopulos
    Abstract:

    Constraint-based rule miners find all rules in a given data-set meeting user-specified constraints such as Minimum support and Confidence. We describe a new algorithm that directly exploits all user-specified constraints including Minimum support, Minimum Confidence, and a new constraint that ensures every mined rule offers a predictive advantage over any of its simplifications. Our algorithm maintains efficiency even at low supports on data that is dense (e.g. relational tables). Previous approaches such as Apriori and its variants exploit only the Minimum support constraint, and as a result are ineffective on dense data due to a combinatorial explosion of “frequent itemsets”.

  • regular paper constraint based rule mining in large dense databases
    2000
    Co-Authors: Roberto J Bayardo, Rakesh Agrawal, Dimitrios Gunopulos
    Abstract:

    Constraint-based rule miners find all rules in a given data-set meeting user-specified constraints such as Minimum support and Confidence. We describe a new algorithm that directly exploits all user-specified constraints including Minimum support, Minimum Confidence, and a new constraint that ensures every mined rule offers a predictive advantage over any of its simplifications. Our algorithm maintains efficiency even at low supports on data that is dense (e.g. relational tables). Previous approaches such as Apriori and its variants exploit only the Minimum support constraint, and as a result are ineffective on dense data due to a combinatorial explosion of "frequent itemsets".

  • constraint based rule mining in large dense databases
    International Conference on Data Engineering, 1999
    Co-Authors: Roberto J Bayardo, Rakesh Agrawal, Dimitrios Gunopulos
    Abstract:

    Constraint-based rule miners find all rules in a given dataset meeting user-specified constraints such as Minimum support and Confidence. We describe a new algorithm that directly exploits all user-specified constraints including Minimum support, Minimum Confidence, and a new constraint that ensures every mined rule offers a predictive advantage over any of its simplifications. Our algorithm maintains efficiency even at low supports on data that is dense (e.g. relational data). Previous approaches such as Apriori and its variants exploit only the Minimum support constraint, and as a result are ineffective on dense data date to a combinatorial explosion of "frequent itemsets".

  • ICDE - Constraint-based rule mining in large, dense databases
    Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337), 1999
    Co-Authors: Roberto J Bayardo, Rakesh Agrawal, Dimitrios Gunopulos
    Abstract:

    Constraint-based rule miners find all rules in a given dataset meeting user-specified constraints such as Minimum support and Confidence. We describe a new algorithm that directly exploits all user-specified constraints including Minimum support, Minimum Confidence, and a new constraint that ensures every mined rule offers a predictive advantage over any of its simplifications. Our algorithm maintains efficiency even at low supports on data that is dense (e.g. relational data). Previous approaches such as Apriori and its variants exploit only the Minimum support constraint, and as a result are ineffective on dense data date to a combinatorial explosion of "frequent itemsets".

Chen Yi-ming - One of the best experts on this subject based on the ideXlab platform.

  • Constraint association-rule mining algorithm based on transaction clip
    Computer Engineering, 2005
    Co-Authors: Chen Yi-ming
    Abstract:

    Aiming at a familiar and simple constraint that some items must or must not present in rules,a fast clipped-transaction-based constraint association-rule mining algorithm was put forward.This algorithm firstly scanned data base to clip transactions horizontally and vertically,then mined frequent item sets from clipped data set to form rules' candidate head sets,body sets and rule item sets.Finally,it scanned original data base again to gain association rules according to Minimum Confidence constraint.Experiments show that,compared with common strategy of succinct constraint,this algorithm has better performance.