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Shanmugapriya, B.
- An Enhanced Projected Clustering Algorithm for High Dimensional Space
Authors
1 Department of Computer Science, Sri Ramakrishna College of Arts and Science for Women, Coimbatore, IN
2 Department of Computer Science Dr.SNS College of Arts and Science, Coimbatore, IN
3 Department of Computer Science and Engineering, Park College of Engineering & Technology, Coimbatore, IN
Source
Data Mining and Knowledge Engineering, Vol 3, No 2 (2011), Pagination: 104-109Abstract
Clustering is a data mining technique for identifying groups in the data set based on some similarity measure. Clustering high dimensional data has been a major challenge due to the inherent sparsity of the points. Most existing clustering algorithms become substantially inefficient if the required similarity measure is computed between data points in the full dimensional space. A number of projected clustering algorithms have been proposed to overcome the above issue. This led to the development of a robust partitional distance based projected clustering algorithm based on K-means algorithm with the computation of distance restricted to subsets of attributes with dense object values. The algorithm is capable of detecting projected clusters of low dimensionality embedded in a high-dimensional space and avoids the computation of the distance in full-dimensional space. The algorithm has been demonstrated using synthetic and real datasets.Keywords
Clustering, High Dimensional Data, Projected Cluster, K-Means Clustering, Subspace Clustering.
- A Study on Prioritization of Test Cases during Software Execution Based on the Dependency
Authors
1 Department of Computer Science, Sri Ramakrishna College of Arts and Science for Women, Coimbatore, IN
Source
Software Engineering, Vol 10, No 4 (2018), Pagination: 75-77Abstract
Ordering of the Test case is used during execution in order to determine the fault based on the functional dependency. Test case prioritization is used for increasing the rate of fault detection. In this paper, a study on test case prioritization is analysed in term of functional dependencies between the codes during execution of the test cases. In this study, A common strategyfor handling test case dependencies is to group these fine-grained tests with dependencies into coarse-grained tests. The nature of the techniques preserves the dependencies in the test ordering. The hypothesis of this work is that dependencies between tests arerepresentative of interactions in the system under test, and executing complex interactions earlier is likely to increase the fault detection rate.To handle the implication on this study, we propose a Hierarchy Process (AHP) can be considered the reference method among those which are based on the case-based paradigm.