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Boopathybabu, A.
- Company Trend Analysis Using Subspace Clustering and Frequent Patterns
Authors
1 Park‟s College, Chinnakari, IN
2 Dept of computer Science, Park‟s College, Chinnakari, IN
Source
Software Engineering, Vol 6, No 8 (2014), Pagination: 223-225Abstract
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.
Keywords
Clustering, Centroid Based 3D-Subspace Clustering, Singular Vector Decomposition.
- Cancer Gene Expression Analysis Using Class Chunk
Authors
1 Park's College, Chinnakkarai, IN
2 Dept. of Computer Science, Park's College, Chinnakkarai, IN
Source
Data Mining and Knowledge Engineering, Vol 6, No 8 (2014), Pagination: 335-339Abstract
Classification consists of assigning a class label to a set of unclassified cases. Supervised and unsupervised classification methods are used to assign class labels. Classification is performed in two steps learning or training and testing. Learning process is used to identify the class patterns from the labeled transactions. In training phase unlabeled transactions are assigned with the class values with reference to the learned class patterns. Bayesian classification and decision tree classification methods are used for the category assignment process. An outlier is a comment that varies so much from other comments as to produce suspicions. Distance based outlier detection methods are used to find records that are differ from the rest of the data set.Critical nuggets are collections of records that have domain-specific with essential information. Nuggets are referred as class chunks. Nuggets are used to perform label or category assignment to transactions. Domains independent method is used to measure criticality and reduce the find space for identify critical nuggets. Criticality measure is the records that detached together from the data set or from values of attributes. Criticality Score (CR-score) indicates the outcome of removing a nearby data's on a classification model. Here we are using three kinds of algorithms. One is Get Nugget Score algorithm is used to calculate the CR-score value. Second is Find boundary algorithm is used to identify the class boundary values. Third is Find critical nuggets algorithm. We can split Find critical nuggets algorithm into two phases to detect critical nuggets for two classes. The centroid neighborhood relationship is used to find the nuggets for the significant classes.
To support this critical nugget for multiple classes we use identification and classification scheme under cancer gene expression environment. The scheme can be accepted to handle mixed attribute data values. To reduce the detection complexity we use the boundary approximation algorithm. With the help of Post processing operations we can able to identify class in multiple data environment.