System and method for dynamic data clustering
First Claim
1. A method for dynamically identifying clusters of related data comprising:
- launching a probe from a first position in an M-dimensional space, said M-dimensional space having a plurality of data points, each of said plurality of data points associated with a data record, each data record having at least M number of data fields;
determining a new position for said probe in said M-dimensional space based on a current position of said probe relative to at least a portion of said plurality of data points in said M-dimensional space;
moving said probe from said current position to said new position;
repeating said determining a new position for said probe until said new position and said current position are approximately a same position;
dynamically identifying a cluster upon determining said same position in said M-dimensional space.
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Abstract
A system and method for dynamically identifying clusters of related data in a database uses a probe to identify the clusters. These clusters, also known as density patterns, are identified by launching the probe from an initial position in a data space associated with the data comprised of a plurality of data points. Each of the data points attracts the probe to itself. Distant data points attract the probe to a lesser extent than do proximate data points. In this manner, the probe is drawn along a trajectory toward an equilibrium point. Once the equilibrium point is reached, a cluster is identified and its location optionally stored. Additional probes are launched from different initial positions in the data space to identify other clusters that may exist in the data space until no unique clusters are identified. The collection of identified clusters is representative of a number and, in some embodiments of the present invention, a general location of related data within the data space.
34 Citations
25 Claims
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1. A method for dynamically identifying clusters of related data comprising:
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launching a probe from a first position in an M-dimensional space, said M-dimensional space having a plurality of data points, each of said plurality of data points associated with a data record, each data record having at least M number of data fields;
determining a new position for said probe in said M-dimensional space based on a current position of said probe relative to at least a portion of said plurality of data points in said M-dimensional space;
moving said probe from said current position to said new position;
repeating said determining a new position for said probe until said new position and said current position are approximately a same position;
dynamically identifying a cluster upon determining said same position in said M-dimensional space. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16)
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17. A method for dynamically identifying a number of clusters of related data from a plurality of data records each having a plurality of data fields, the data represented as N data points in an M-dimensional space where M is less than or equal to a number of the plurality of data fields and N is less than or equal to a number of the plurality of data records, the method comprising:
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initializing a current position of a data probe as a first position in the M-dimensional space;
determining a new position for said data probe in the M-dimensional space based on a similarity between said data probe as indicated by said current position and at least a portion of the N data points in the M-dimensional space;
adjusting said current position of said data probe to said new position;
repeating said determining a new position and said adjusting said current position until said new position and said current position are approximately a same position; and
once said new position and said current position are approximately said same position, incrementing a count of the number of clusters of related data. - View Dependent Claims (18, 19, 20, 21, 22, 23, 24, 25)
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Specification