Data mining method and system using regression clustering
First Claim
Patent Images
1. A method, comprising:
- a processor which performs the following;
selecting a set number of functions correlating variable parameters of a dataset; and
clustering the dataset by iteratively applying a regression algorithm and a K-Harmonic Means performance function on the set number of functions to determine a pattern in said dataset;
wherein said clustering comprises determining distances between data points of the dataset and values correlated with the set number of functions, regressing the set number of functions using data point probability and weighting factors associated with the determined distances, calculating a difference of harmonic averages for the distances determined prior to and subsequent to said regressing, and repeating said regressing, determining and calculating upon determining the difference of harmonic averages is greater than a predetermined value.
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Abstract
A method and a system are provided which regressively cluster datapoints from a plurality of data sources without transferring data between the plurality of data sources. In addition, a method and a system are provided which mine data from a dataset by iteratively applying a regression algorithm and a K-Harmonic Means performance function on a set number of functions derived from the dataset.
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Citations
22 Claims
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1. A method, comprising:
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a processor which performs the following; selecting a set number of functions correlating variable parameters of a dataset; and clustering the dataset by iteratively applying a regression algorithm and a K-Harmonic Means performance function on the set number of functions to determine a pattern in said dataset; wherein said clustering comprises determining distances between data points of the dataset and values correlated with the set number of functions, regressing the set number of functions using data point probability and weighting factors associated with the determined distances, calculating a difference of harmonic averages for the distances determined prior to and subsequent to said regressing, and repeating said regressing, determining and calculating upon determining the difference of harmonic averages is greater than a predetermined value. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A storage medium comprising program instructions executable by a processor for:
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selecting a set number of functions correlating variable parameters of a dataset; determining distances between datapoints of the dataset and values correlated with the set number of functions; calculating harmonic averages of the distances; regressing the set number of functions using datapoint probability and weighting factors associated with the determined distances; repeating said determining and calculating for the regressed set of functions; computing a change in harmonic averages for the set number of functions prior to and subsequent to said regressing; and reiterating said regressing, repeating and computing upon determining the change in harmonic averages is greater than a predetermined value to thereby determine a pattern in said dataset. - View Dependent Claims (9, 10, 11, 12, 13)
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14. A system, comprising:
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an input port configured to receive data; and a processor configured to; regress functions correlating variable parameters of a set of the data; cluster the functions using a K-Harmonic Mean performance function; and repeat said regress and cluster sequentially to thereby determine a pattern in said set of data; wherein the processor clusters the functions by determining distances between data points of the dataset and values correlated with a set number of functions, regressing the set number of functions using data point probability and weighting factors associated with the determined distances, calculating a difference of harmonic averages for the distances determined prior to and subsequent to said regressing. - View Dependent Claims (15, 16)
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17. A system, comprising:
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a plurality of data sources; and a means for regressively clustering datapoints from the plurality of data sources without transferring data between the plurality of data sources to thereby determine a pattern in data contained in said data sources and for applying a K-Harmonic Means performance function on the data; wherein the means for regressively clustering the datasets comprises a storage medium with program instructions executable using a processor for selecting a set number of functions correlating variable parameters of a dataset, determining distances between data points of the dataset and values correlated with the set number of functions, regressing the set number of functions using data point probability and weighting factors associated with the determined distances, calculating a difference of harmonic averages for the distances determined prior to and subsequent to said regressing; and
reiterating said regressing, determining and calculating upon determining the difference of harmonic averages is less than a predetermined value. - View Dependent Claims (18)
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19. A system, comprising:
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a plurality of data sources each having a processor configured to access datapoints within the respective data source; and a central station coupled to the plurality of data sources and comprising a processor, wherein the processors of the central station and plurality of data sources are collectively configured to mine the datapoints of the data sources as a whole without transferring all of the datapoints between the data sources and the central station to thereby determine a pattern in datapoints contained in said data sources; wherein the each of the processors within the plurality of data sources is configured to regressively cluster a dataset within the respective data source; wherein the processor within the central station is configured to; collect information pertaining to the regressively clustered datasets; based upon the collected information, calculate common coefficient vectors which balance variations between functions correlating similar variable parameters of the regressively clustered datasets; compute a residual error from the common coefficient vectors; propagate the common coefficient vectors to the data sources upon computing a residual error value greater than a predetermined value; and send a message to the data sources to terminate the regression clustering of the datasets upon computing a residual error value less than a predetermined value.
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20. A processor-based method for mining data, comprising:
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independently applying a regression clustering algorithm to a plurality of distributed datasets by determining distances between data points of each dataset and values correlated with a set number of functions, regressing the set number of functions using data point probability and weighting factors associated with the determined distances, calculating a difference of harmonic averages for the distances determined prior to and subsequent to application of said regression algorithm, and repeating said regressing, determining and calculating upon determining the difference of harmonic averages is greater than a predetermined value; developing matrices from probability and weighting factors computed from the regression clustering algorithm, wherein the matrices individually represent the distributed datasets without including all datapoints within the datasets; determining global coefficient vectors from a composite of the matrices; and multiplying functions correlating similar variable parameters of the distributed datasets by the global coefficient vectors to thereby determine a pattern in said datasets. - View Dependent Claims (21, 22)
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Specification