ALBA System for Automated Clustering Validation

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Solution Overview

Problem

Current segmentation analysis in data science is time-consuming and repetitive, often requiring expertise that is not shared, and existing clustering algorithms provide inconsistent results due to varying metrics and computational inefficiencies.

Innovation Solution

An artificial learning blended algorithm (ALBA) system with an optimum cluster module and validation cluster module is implemented, which determines the optimal number of clusters using multiple metrics and validates clustering algorithms using index techniques like ELECTRE and TOPSIS to find the best clustering method, minimizing intra-cluster distance and maximizing inter-cluster distance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If multiple clustering algorithms are implemented with separate analysis for each algorithm, then comprehensive segmentation results can be obtained, but the analysis process becomes time-consuming and repetitive

Engineering Contradiction:

Improvesegmentation accuracyVSAvoidanalysis time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent combines multiple clustering algorithms (k-means, hierarchical, DBSCAN) into a single unified system that processes all algorithms simultaneously. The system integrates separate analysis workflows into one coordinated process, eliminating repetitive manual operations while maintaining comprehensive segmentation results across all algorithms.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system creates a universal segmentation platform that handles multiple clustering algorithms through a common interface and unified processing framework. This multi-functional system can analyze different algorithms using the same infrastructure, reducing the need for separate analysis setups and decreasing overall processing time.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Measurement precision

If segmentation analysis is performed manually by experienced personnel, then accurate results can be achieved, but the knowledge and experience cannot be shared with others

Engineering Contradiction:

Improvesegmentation accuracyVSAvoidknowledge sharing capability

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The system enables automated segmentation analysis that operates independently without requiring continuous expert intervention. By encoding expert knowledge into the system's algorithms and validation rules, the platform performs self-service analysis, producing consistent results without relying on individual expert personnel, thereby making the capability shareable and reproducible.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system captures and replicates expert segmentation knowledge into a standardized digital framework. By translating manual expert processes into algorithmic rules and validation criteria, the system creates copyable, transferable knowledge that can be consistently applied across different users and applications without losing accuracy.

Inventive Principle:

Principle #26Copying

3Adaptability or versatility

If existing clustering algorithms are used with varying metrics, then different analytical perspectives can be obtained, but inconsistent results are produced

Engineering Contradiction:

Improveanalytical flexibilityVSAvoidresult consistency

Core Design Contradiction:

Adaptability or versatilityVSStability of the object's composition

Solution Approach 1:

The system standardizes the metrics and parameters used across different clustering algorithms by implementing a unified evaluation framework. It normalizes distance measurements, clustering criteria, and validation metrics so that all algorithms operate with consistent parameters, eliminating result inconsistencies while preserving the ability to apply different algorithmic approaches.

Inventive Principle:

Principle #35Parameter changes

4Reliability

If comprehensive validation of clustering algorithms is performed, then the best algorithm can be identified, but the process becomes more complex

Engineering Contradiction:

Improvealgorithm validation accuracyVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The validation process is divided into distinct modular stages: initial clustering execution, intermediate validation checks, and final algorithm selection. Each stage handles specific validation tasks independently, making the overall complex validation process manageable through structured segmentation of validation functions.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system introduces an intermediary validation layer that sits between algorithm execution and final results. This intermediary component automatically performs consistency checks, metric comparisons, and quality assessments, simplifying the validation process by handling complexity in a dedicated intermediate stage rather than requiring users to manage all validation details directly.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS11487964B2Comprehensive data science solution for segmentation analysis

Publication Date: 2022.11.01 DELL PROD LP

AI summary

A system, method, and computer-readable medium are disclosed for improved segmentation analysis. In various embodiments, an artificial learning blended algorithm (ALBA) system—is implemented. In various embodiments, the ALBA system includes an optimum cluster module to determine an optimum number of clusters for multiple clustering algorithms, and a validation cluster module to validate cluster algorithms using index validation techniques to determine a clustering algorithm from multiple clustering algorithms that use the determined optimum number of clusters.