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.

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.

Development of Hierarchical Clustering Techniques for Gridded Data from Mixed Data Sequences

Knowledge discovery in databases is the non-trivial process of identifying valid, novel potentially useful and ultimately understandable patterns from data. The term Data mining refers to the process which does the exploratory analysis on the data and builds some model on the data. To infer patterns from data, data mining involves different approaches like association rule mining, classification techniques or clustering techniques. Among the many data mining techniques, clustering plays a major role, since it helps to group the related data for assessing properties and drawing conclusions. Most of the clustering algorithms act on a dataset with uniform format, since the similarity or dissimilarity between the data points is a significant factor in finding out the clusters. If a dataset consists of mixed attributes, i.e. a combination of numerical and categorical variables, a preferred approach is to convert different formats into a uniform format. The research study explores the various techniques to convert the mixed data sets to a numerical equivalent, so as to make it equipped for applying the statistical and similar algorithms. The results of clustering mixed category data after conversion to numeric data type have been demonstrated using a crime data set. The thesis also proposes an extension to the well known algorithm for handling mixed data types, to deal with data sets having only categorical data. The proposed conversion has been validated on a data set corresponding to breast cancer. Moreover, another issue with the clustering process is the visualization of output. Different geometric techniques like scatter plot, or projection plots are available, but none of the techniques display the result projecting the whole database but rather demonstrate attribute-pair wise analysis