Data processing: database and file management or data structures – Database design – Data structure types
Patent
1996-10-25
1999-02-09
Lintz, Paul R.
Data processing: database and file management or data structures
Database design
Data structure types
707104, 705 10, G06F 1730
Patent
active
058707484
ABSTRACT:
A method is disclosed for determining the correlation among data sets having a numerical attribute and a 0-1 attribute. First, a numerical attribute is divided into a plurality of buckets, and each data set is placed into a single bucket according to the value of the numerical attribute. The number of data sets in each bucket and the number of data sets with a 0-1 attribute of 1 are counted. Second, an axis corresponding to the total number of data sets in a first through a particular buckets (X axis) and an axis corresponding to the total number of data sets with a 0-1 attribute of 1 in a first through a particular buckets (Y axis) are virtually established, and points corresponding to the respective values of the first through the particular buckets are virtually plotted. Third, after a plane is constructed in this manner, one of the pairs of points separated at an interval of T.times.N or T or larger which has the largest slope is found. This step is most important to fast processing, and this invention employs the nature of convex hulls to reduce the number of points to be considered. Finally, once this pair of points has been determined, the corresponding pair of buckets can be determined, resulting in the output of the corresponding segment. Also, once this process has been finished, the user can retrieve the required part of the data included in this section.
REFERENCES:
patent: 5365426 (1994-11-01), Siegel et al.
patent: 5615341 (1997-03-01), Agrawal et al.
DeLeo, J.M., "Receiver Operating Characteristic Laboratory (ROCLAB): Software for Developing Decision Strategies That Account For Uncertainty", (Apr. 1993) Proceedings Second International Symposium on Uncertainty Modeling and Analysis, College Park, Md., 25.
et al., "Using Upper Bounds on Attainable Discrimination to Select Discrete Valued Features", Neural Networks For Signal Processing VI. Proceedings of the 1996 IEEE Signal Processing Society Workshop, 4-6 Sep. 1996, Kyoto Japan, pp. 233-242.
R. Agrawal et al., "Mining Association Rules Between Sets of Items in Large Databases", Proc. of the ACM SIGMOD Conference on Management of Data, Washington, DC, May 1993, pp. 207-216.
R. Agrawal et al., "Database Mining: A Performance Perspective", IEEE Transactions on Knowledge and Data Engineering, Special Issue on Learning and Discovery in Knowledge-based Databases, Dec. 1993, pp. 914-925.
R. Agrawal et al., "Fast Algorithms for Mining Association Rules", Proc. of the VLDB Conference, Santiago, Chile, Sep. 1994, pp. 487-499.
R. Agrawal et al., "Mining Sequential Patterns", Proc. of the International Conference on Data Engineering, Mar. 1995, pp. 3-14.
J. Han et al., "Discovery of Multiple-level Association Rules from Large Databases", Proc. of the VLDB Conference, Zurich, Switzerland, Sep. 1995, pp. 420-431.
M. Houtsma et al., "Set-oriented Mining for Association Rules in Relational Databases", Proc. of the 11th Conference on Data Engineering, 1995, pp. 25-33.
H. Mannila et al., "Improved Methods for Finding Association Rules", Pub. No. C-1993-65, University of Helsinki, 1993.
J.S. Park et al., "An Effective Hash-based Algorithm for Mining Association Rules", Proc. of the ACM SIGMOD Conference on Management of Data, San Jose, California, May 1995, pp. 175-186.
A. Savasere et al., "An Efficient Algorithm for Mining Association Rules in Large Databases", Proc. of the 21st VLDB Conference, Zurich, Switzerland, Sep. 1995, pp. 432-444.
G. P. Shapiro, "Discovery, Analysis, and Presentation of Strong Rules", Knowledge Discovery in Databases, AAAI/MIT Press, Menlo Park, California, 1991, pp. 229-248.
Fukuda Takeshi
Morimoto Yasuhiko
Morishida Shinichi
Tokuyama Takeshi
International Business Machines - Corporation
Lintz Paul R.
Tran Khanh Q.
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