Widespread placement and high data sampling rate of current generation of phase measurement units (PMUs) in wide area monitoring systems result in huge amount of data to be analysed and stored, making efficient storage of such data a priority. This paper presents a generalized compression technique that utilizes the inherent correlation within PMU data by exploiting both spatial and temporal redundancies. A two stage compression algorithm is proposed using principal component analysis in the first stage and discrete cosine transform in the second. Since compression parameters need to be adjusted to compress critical disturbance information with high fidelity, an automated but simple statistical change detection technique is proposed to identify disturbance data. Extensive verifications are performed using field data, as well as simulated data to establish generality and superior performance of the method. In our project we consider an ecosystem, Ecology plays an important role in agriculture crop rotation, weed control, management of grasslands, range management forestry, biological surveys, pest control, fishery biology, and in the conservation of soil, wildlife, forest, water supplies, water bodies like rivers, lakes and ponds, Collecting related features of living things in the ecosystem and creates a database. And applying the data extraction based on the conditions. Afterward, we propose a compression algorithm, called 2P2D, which exploits the obtained group movement patterns to reduce the amount of delivered data.

1. International Conference on Advances & Challenges in Interdisciplinary Engineering and Management 51
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International Conference on Advances & Challenges in Interdisciplinary Engineering and
Management 2017 [ICACIEM 2017]
ISBN 978-81-933235-1-9 VOL 01
Website icaciem.org eMail icaciem@asdf.res.in
Received 10 – January – 2017 Accepted 28 - January – 2017
Article ID ICACIEM101 eAID ICACIEM.2017.101
FR-Efficient Compression of PUM Data in WAMS
S Jayaprakash1
, D Manikandan2
, S Mathan3
, S Parthipan4
, C Vijayakumar5
, S Roshini6
1,2,3,4,5
III Year Students, 6
Assistant Professor, Dept. of CSE, Vidyaa Vikas College of Engineering and Technology, Tiruchengode, India
Abstract: Widespread placement and high data sampling rate of current generation of phase measurement units (PMUs) in wide area monitoring
systems result in huge amount of data to be analysed and stored, making efficient storage of such data a priority. This paper presents a generalized
compression technique that utilizes the inherent correlation within PMU data by exploiting both spatial and temporal redundancies. A two stage
compression algorithm is proposed using principal component analysis in the first stage and discrete cosine transform in the second. Since compression
parameters need to be adjusted to compress critical disturbance information with high fidelity, an automated but simple statistical change detection
technique is proposed to identify disturbance data. Extensive verifications are performed using field data, as well as simulated data to establish
generality and superior performance of the method. In our project we consider an ecosystem, Ecology plays an important role in agriculture crop
rotation, weed control, management of grasslands, range management forestry, biological surveys, pest control, fishery biology, and in the conservation
of soil, wildlife, forest, water supplies, water bodies like rivers, lakes and ponds, Collecting related features of living things in the ecosystem and creates
a database. And applying the data extraction based on the conditions. Afterward, we propose a compression algorithm, called 2P2D, which exploits the
obtained group movement patterns to reduce the amount of delivered data.
ISBN 978-81-933235-1-9 VOL 01
Website icaciem.org eMail icaciem@asdf.res.in
Received 10 – January – 2017 Accepted 28 - January – 2017
Article ID ICACIEM102 eAID ICACIEM.2017.102
Cluster Analysis of Organizational Website using Fk-
Medoids
C Gomathi1
1
Professor, Department of MCA, Vidyaa Vikas College of Engineering and Technology, Tiruchengode, India
Abstract: Web Usage Mining (WUM) can be used to determine if the information architecture of a web site is structured correctly. Most of the
existing WUM tools are not used to effective website visualization of the results obtained. Clustering techniques are used as the basis of many existing
approaches for developing website visualization. A new clustering technique is introduced and used as the basic technique for visualization in order to
evaluate the information architecture of an organization website. The performance of the proposed systems is evaluated using real data. Here, a new
efficient clustering technique Fuzzy K-Medoids (FK-Medoids) has been proposed. The proposed algorithm uses Kongu Arts and Science College (KASC)
and KSR College of Engineering (KSRCE) web log files for clustering after pre-processing. The results of the experiments show the efficiency of the
improved algorithm and suggest that dimension of learning styles i.e. preferences to learning material, can be modelled using suitable attributes and can
be detected using data mining techniques.
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2017] which is published by ASDF International, Registered in London, United Kingdom under the directions of the Editor-in-Chief Dr K Samidurai and Editors
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