SPATIAL CLUSTERING ALGORITHMS-AN OVERVIEW.

An Overview of known spatial clustering algorithms The space of interest can be the two-dimensional abstraction of the surface of the earth or a man-made space like the layout of a VLSI design, a volume containing a model of the human brain, or another 3d-space representing the arrangement of chains of protein molecules. The data consists of geometric information and can be either discrete or continuous. The explicit location and extension of spatial objects define implicit relations of spatial neighborhood (such as topological, distance and direction relations) which are used by spatial data mining algorithms. Therefore, spatial data mining algorithms are required for spatial characterization and spatial trend analysis. Spatial data mining or knowledge discovery in spatial databases differs from regular data mining in analogous with the differences between non-spatial data and spatial data. The attributes of a spatial object stored in a database may be affected by the attributes of the spatial neighbors of that object. In addition, spatial location, and implicit information about the location of an object, may be exactly the information that can be extracted through spatial data mining

Design and Development of data mining models for the predictions of manpower placement in the technical Domain

Data mining is one of the hottest research areas nowadays as it has got wide variety of applications in common man’s life to make the world a better place to live. It is all about finding interesting hidden patterns in a huge history data base. As an example, from a sales data base, one can find an interesting pattern like “people who buy magazines tend to buy news papers also” using data mining. Now in the sales point of view the advantage is that one can place these things together in the shop to increase sales. In this research work, data mining is effectively applied to a domain called placement chance prediction, since taking wise career decision is so crucial for anybody for sure. In India technical manpower analysis is carried out by an organization named National Technical Manpower Information System (NTMIS), established in 1983-84 by India's Ministry of Education & Culture. The NTMIS comprises of a lead centre in the IAMR, New Delhi, and 21 nodal centres located at different parts of the country. The Kerala State Nodal Centre is located at Cochin University of Science and Technology. In Nodal Centre, they collect placement information by sending postal questionnaire to passed out students on a regular basis. From this raw data available in the nodal centre, a history data base was prepared. Each record in this data base includes entrance rank ranges, reservation, Sector, Sex, and a particular engineering. From each such combination of attributes from the history data base of student records, corresponding placement chances is computed and stored in the history data base. From this data, various popular data mining models are built and tested. These models can be used to predict the most suitable branch for a particular new student with one of the above combination of criteria. Also a detailed performance comparison of the various data mining models is done.This research work proposes to use a combination of data mining models namely a hybrid stacking ensemble for better predictions. A strategy to predict the overall absorption rate for various branches as well as the time it takes for all the students of a particular branch to get placed etc are also proposed. Finally, this research work puts forward a new data mining algorithm namely C 4.5 * stat for numeric data sets which has been proved to have competent accuracy over standard benchmarking data sets called UCI data sets. It also proposes an optimization strategy called parameter tuning to improve the standard C 4.5 algorithm. As a summary this research work passes through all four dimensions for a typical data mining research work, namely application to a domain, development of classifier models, optimization and ensemble methods.

IR: Knowledge Creation, Knowledge Management and Knowledge Dissemination

Open access iiiovemerit and open source software movement plays an important role in creation of knowledge, knowledge management and knowledge dissemination. Scholarly communication and publishing are increasingly taking place in the electronic environment. With a growing proportion of the scholarly record now existing only in digital format, serious issues regarding access and preservation are being raised that are central to future scholarship. Institutional Repositories provide access to past. present and future scholarly literature and research documentation; ensures its preservation; assists users in discovery and use; and offers educational programs to enable users to develop lifelong literacy. This paper explores these aspects on how IR of Cochin University of Science & Technology supports scientific community for knowledge creation. knowledge Management, and knowledge dissemination.

Applying Data mining using Statistical Techniques for Career Selection

For years, choosing the right career by monitoring the trends and scope for different career paths have been a requirement for all youngsters all over the world. In this paper we provide a scientific, data mining based method for job absorption rate prediction and predicting the waiting time needed for 100% placement, for different engineering courses in India. This will help the students in India in a great deal in deciding the right discipline for them for a bright future. Information about passed out students are obtained from the NTMIS ( National technical manpower information system ) NODAL center in Kochi, India residing in Cochin University of science and technology

A Data Mining Approach for the Identification of Adverse Drug Events (ADE) resulting from Drug-Drug Interactions (DDI) to improve pharmacovigilance

In the current study, epidemiology study is done by means of literature survey in groups identified to be at higher potential for DDIs as well as in other cases to explore patterns of DDIs and the factors affecting them. The structure of the FDA Adverse Event Reporting System (FAERS) database is studied and analyzed in detail to identify issues and challenges in data mining the drug-drug interactions. The necessary pre-processing algorithms are developed based on the analysis and the Apriori algorithm is modified to suit the process. Finally, the modules are integrated into a tool to identify DDIs. The results are compared using standard drug interaction database for validation. 31% of the associations obtained were identified to be new and the match with existing interactions was 69%. This match clearly indicates the validity of the methodology and its applicability to similar databases. Formulation of the results using the generic names expanded the relevance of the results to a global scale. The global applicability helps the health care professionals worldwide to observe caution during various stages of drug administration thus considerably enhancing pharmacovigilance

Multichannel Remote Data Acquisition and Analysing Systems For Fisheries / Environmental Investigations

Remote Data acquisition and analysing systems developed for fisheries and related environmental studies have been reported. It consists of three units. The first one namely multichannel remote data acquisition system is installed at the remote place powered by a rechargeable battery. It acquires and stores the 16 channel environmental data on a battery backed up RAM. The second unit called the Field data analyser is used for insitue display and analysis of the data stored in the backed up RAM. The third unit namely Laboratory data analyser is an IBM compatible PC based unit for detailed analysis and interpretation of the data after bringing the RAM unit to the laboratory. The data collected using the system has been analysed and presented in the form of a graph. The system timer operated at negligibly low current, switches on the power to the entire remote operated system at prefixed time interval of 2 hours.Data storage at remote site on low power battery backedupRAM and retrieval and analysis of data using PC are the special i ty of the system. The remote operated system takes about 7 seconds including the 5 second stabilization time to acquire and store data and is very ideal for remote operation on rechargeable bat tery. The system can store 16 channel data scanned at 2 hour interval for 10 days on 2K backed up RAM with memory expansion facility for 8K RAM.

Information Dynamics and Knowledge Management in Science & Technology

This paper describes the current information dynamics and its effect in higher education and research in science and technology. Open access movement ,Institutional repositories ,Digital libraries,Knowledge gateways,Blogs,Wikis,and social bookmark tools have rapidly emerged on the web creating a new scenerio that radically changes the knowledge production process such as the creation of information,formats and sources of information,coding and processing ,accessing managing sharing and dissemination of information.The management of knowledge created by academia of Cochin University Of Science And Technology is examined in this challenging context of information dynamics.