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Nageswara Rao, K.
- Effective Implementation of Agile Practices – A Collaborative and Innovative Framework
Abstract Views :259 |
PDF Views:3
Authors
Affiliations
1 Velagapudi Ramakrishna Siddhartha Engineering College in the Department of Computer Applications, Vijayawada – 520 007, IN
2 Prasad V. Potluri Siddhartha Institute of Technology in the Department of Computer Science and Engineering, Vijayawada – 520 007, IN
1 Velagapudi Ramakrishna Siddhartha Engineering College in the Department of Computer Applications, Vijayawada – 520 007, IN
2 Prasad V. Potluri Siddhartha Institute of Technology in the Department of Computer Science and Engineering, Vijayawada – 520 007, IN
Source
Software Engineering, Vol 2, No 9 (2010), Pagination: 191-196Abstract
This paper introduces a collaborative and innovative framework of agile software development that leads to a software product that proves in practice to be of much higher quality than what traditional software teams usually deliver. Agile methods place more emphasis on people, interaction, working software, customer collaboration, and change rather than on tools, processes contracts and plans. A number of new methodologies claiming these agile principles have been introduced. Each method has its own active research and user communities.Keywords
Communication, Continuous Integration, Framework, Metrics.- A Novel Clustering Data Based on K-Means
Abstract Views :317 |
PDF Views:2
Authors
Affiliations
1 Department of CSE, SBCE, Khammam, IN
2 Department of CSE, Mother Teresa Institute of Science and Technology, Sattupally, IN
3 Department of CSE, KITS, Khammam, IN
1 Department of CSE, SBCE, Khammam, IN
2 Department of CSE, Mother Teresa Institute of Science and Technology, Sattupally, IN
3 Department of CSE, KITS, Khammam, IN
Source
Data Mining and Knowledge Engineering, Vol 4, No 6 (2012), Pagination: 303-308Abstract
In this paper a new algorithm for clustering symbolic data based on K-Means algorithm is proposed. This new algorithm allows the data entry and the membership degree to be intervals. In our approach, we propose a dynamic document clustering based on structured MARDL technique. In this method, each document is assigned a weight by term frequency and inverse document frequency method using cosine similarity measure and then, the documents are first separated into clusters using k-Means method. The largest cluster will split and forms two sub clusters and this step would be repeated for many times until clusters formed are with high similarity. In addition, our approach tends to capture the intrinsic structure of a data set, e.g., the number of clusters. Simulation results demonstrate that our approach yields favorite results for a variety of temporal data clustering tasks. As our weighted cluster ensemble algorithm can combine any input partitions to generate a clustering ensemble, we also investigate its limitation by formal analysis and empirical studies.Keywords
Clustering, K-Means, MARDAL.- Effective Independent Quality Assessment Using IVandV
Abstract Views :385 |
PDF Views:185
Authors
Affiliations
1 Department of Computer Applications, V.R. Siddhartha Engineering College, Kanuru, Vijayawada-7, IN
2 Department of Computer Science and Engineering, P.V.P. Siddhartha Institute of Technology, Kanuru, Vijayawada-7, IN
1 Department of Computer Applications, V.R. Siddhartha Engineering College, Kanuru, Vijayawada-7, IN
2 Department of Computer Science and Engineering, P.V.P. Siddhartha Institute of Technology, Kanuru, Vijayawada-7, IN