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Company Trend Analysis Using Subspace Clustering and Frequent Patterns

A. Kavitha, A. Boopathybabu


Clustering techniques and frequent pattern mining methods are used to discover events in company data analysis. Feature selection method is used for identifying a subset of the most needed features, it produces compatible results. A feature selection algorithm is constructed with the consideration of efficiency and effectiveness factors.

Data models are analyzed with different dimensions. Object, attribute and context information are linked in the 3 dimensional data models. Cluster quality is decided with domain knowledge and parameter setting requirements.CAT Seeker is also referred as a Centroid Actionable 3D subspace clustering framework. CAT Seeker framework is used to find profitable actions. Singular value decomposition, numerical optimization and 3D frequent itemset mining methods are integrated in CAT Seeker model. Singular value decomposition (SVD) is used to calculating and pruning the homogeneous tensor. Augmented Lagrangian Multiplier Method is used to calculating the probabilities of the values. 3D closed pattern mining is used to fetch Centroid-Based Actionable 3D Subspaces (CATS).

Clustring and pattern mining techniques are integrated in the CATSeeker method. CAT Seeker framework is improved with optimal centroid estimation scheme. Intra cluster accuracy factor is used to fetch centroid values. Inter cluster distance is also considered in centroid estimation process. Dimensionality analysis is applied to improve the subspace selection process.


Clustering, Centroid based 3D- Subspace Clustering, Singular Vector Decomposition.

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