# US 2017/0255688 A1 · Parameter set determination for clustering of datasets

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 2017/0255688 A1
- Document ID: US20170255688A1
- Status: Published application
- Kind: A1
- Application number: 15/057,857
- Filed: 2016-03-01
- Published: 2017-09-07
- Earliest claimed: 2016-03-01
- Assignees: Accenture Global Solutions Limited
- Applicants: Accenture Global Solutions Limited
- Inventors: Emily HACKETT-JONES; Theam Yong CHEW; Stephen KIRKBY; Rob PALLOTTA; Katherine ENDERLING
- Web page: https://patentgrep.com/patent/US20170255688A1

## CPC

- G06F 16/285: {Clustering or classification}
- G06N 3/126: Evolutionary algorithms, e.g. genetic algorithms or genetic programming
- G06N 20/00: Machine learning
- G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A clustering system selects a parameter set from a plurality of parameter sets associated with a dataset to generate a hyper-optimized cluster of the dataset. Different parameter sets are generated by varying the parameter values based on the Genetic Algorithm or a particle swarming algorithm. A parameter set having a high Fitness score is selected from the different parameter sets and a clustered solution produced using the selected parameter set having a maximum total score is used to produce actionable items.

## Claims

### Claim 1

A clustering cycle optimization system comprising:
a data storage to store parameters for a parameter set; and
one or more processors to perform operations to:
generate an optimized parameter set which enables generation of an optimized cluster solution of a dataset by iterating over a plurality of parameter sets;
generate a plurality of cluster solutions from each of a plurality of parameter sets, the plurality of cluster solutions comprising the optimized cluster solution;
select the optimized cluster solution based on a total score of the optimized cluster solution;
select a hyper-optimized parameter set based on its respective fitness score; and
output an actionable item based on the hyper-optimized cluster solution.

### Claim 2 (depends on 1)

The clustering cycle optimization system of claim 1, wherein the operations are further to:
generate an initial parameter set of the plurality of parameter sets via random selection of parameter values.

### Claim 3 (depends on 2)

The clustering cycle optimization system of claim 2, wherein the operations are further to:
cause a random change in a numerical value of a parameter comprised in the plurality of parameter sets.

### Claim 4 (depends on 3)

The clustering cycle optimization system of claim 3, wherein the random change of 5% is caused in the numerical value; and Boolean and categorical variables are re-sampled.

### Claim 5 (depends on 2)

The clustering cycle optimization system of claim 2, wherein the system comprises:
a crossover operator that randomly swaps parameters between two of the plurality of parameter sets.

### Claim 6 (depends on 2)

The clustering cycle optimization system of claim 2, wherein the operations are further to:
set categorical distances based on equality of values, wherein non-equal values are equidistant from each other.

### Claim 7 (depends on 1)

The clustering cycle optimization system of claim 1, wherein the optimized parameter set has a high fitness score relative to fitness scores of other parameter sets for the same data set.

### Claim 8 (depends on 7)

The clustering cycle optimization system of claim 7, wherein the optimized cluster solution has a maximum total score of the plurality of cluster solutions associated with the optimized parameter set.

### Claim 9 (depends on 7)

The clustering cycle optimization of claim 7, wherein the fitness score of the current parameter set is an average of total scores of upper quartile subset of the plurality of cluster solutions.

### Claim 10 (depends on 1)

The clustering cycle optimization of claim 1, wherein the optimized cluster solution includes clusters of data that each have attributes for determining the actionable item.

### Claim 11

A method for hyper-optimizing a clustering engine, comprising:
generating a plurality of cluster solutions corresponding to a parameter set of a dataset;
obtaining a Fitness score for the parameter set;
storing the parameter set in a temporary storage;
changing at least one parameter value of the parameter set; and
iterating through the generating, obtaining, storing and changing steps until an average fitness of a current generation of the parameter set has stabilized to within a given percentage of a previous generation's average fitness;
selecting a cluster solution having a maximum total score from a plurality of cluster solutions as a hyper-optimized cluster solution, the plurality of cluster solutions generated using the current generation of the parameter set; and
generating an actionable item based on the hyper-optimized cluster solution.

### Claim 12 (depends on 11)

The method of claim 11, wherein generating the Fitness score further comprises:
selecting an upper quartile of total scores of the plurality of cluster solutions;
calculating an average of the upper quartile total scores.

### Claim 13 (depends on 11)

The method of claim 11, further comprising:
calculating respective total scores for the plurality of cluster solutions wherein each respective total score is a sum of a business value score, a solution diversity score and an average centroid distance score.

### Claim 14 (depends on 11)

The method of claim 11, wherein changing at least one parameter value of the parameter set comprises:
perturbing at least one parameter value by a random change.

### Claim 15 (depends on 11)

The method of claim 11, wherein changing at least one parameter value of the parameter set comprises:
randomly swapping the parameters of two of the parameter sets.

### Claim 16 (depends on 11)

The method of claim 11, wherein changing at least one parameter value of the parameter set comprises: reselecting categorical parameters randomly from the set of possible values.

### Claim 17 (depends on 11)

The method of claim 11, wherein the dataset represents stores and the actionable items comprises:
selecting a subset of the stores for upgrades.

### Claim 18

A non-transitory computer readable medium including machine readable instructions that are executable by at least one processor to:
generate a plurality of clusters corresponding to a parameter set of a dataset;
obtain a Fitness score for the parameter set;
store the parameter set in a temporary storage;
change at least one parameter value of the parameter set;
iterate through the generating, obtaining, storing and changing when a parameter set is generated through the change that has a higher Fitness score until a predetermined number of iterations are performed;
select a cluster having a maximum total score from a plurality of clusters as a hyper-optimized cluster, the plurality of clusters are associated with one parameter set of the predetermined number of parameter sets, the associated parameter set having a high Fitness score; and
perform an actionable item based on the hyper-optimized cluster.

### Claim 19 (depends on 17)

The non-transitory computer readable medium of claim 17, wherein the machine readable instructions for changing the parameter value include machine readable instructions to:
perturb the at least one parameter value by a random change.

### Claim 20 (depends on 17)

The non-transitory computer readable medium of claim 17, wherein the machine readable instructions for changing the parameter value include machine readable instructions to:
randomly swap the parameters of two of the parameter sets.