Data mining method and system using regression clustering
AssigneeHewlett-Packard Development Company, L.P.
InventorBin Zhang
Life of this patent
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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.
Claims30
A processor-based method, comprising:
- 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.
A storage medium comprising program instructions executable by a processor for:
- 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.
A system, comprising:
- 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.
A system, comprising:
- 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.
A system, comprising:
- 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.
A processor-based method for mining data, comprising:
- independently applying a regression clustering algorithm to a plurality of distributed datasets;
- 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.
Dependencies are inferred from the claim text.