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.

A Novel Decision Tree Algorithm for Numeric Datasets - C 4.5*Stat

Decision trees are very powerful tools for classification in data mining tasks that involves different types of attributes. When coming to handling numeric data sets, usually they are converted first to categorical types and then classified using information gain concepts. Information gain is a very popular and useful concept which tells you, whether any benefit occurs after splitting with a given attribute as far as information content is concerned. But this process is computationally intensive for large data sets. Also popular decision tree algorithms like ID3 cannot handle numeric data sets. This paper proposes statistical variance as an alternative to information gain as well as statistical mean to split attributes in completely numerical data sets. The new algorithm has been proved to be competent with respect to its information gain counterpart C4.5 and competent with many existing decision tree algorithms against the standard UCI benchmarking datasets using the ANOVA test in statistics. The specific advantages of this proposed new algorithm are that it avoids the computational overhead of information gain computation for large data sets with many attributes, as well as it avoids the conversion to categorical data from huge numeric data sets which also is a time consuming task. So as a summary, huge numeric datasets can be directly submitted to this algorithm without any attribute mappings or information gain computations. It also blends the two closely related fields statistics and data mining

Gap Analysis For Implementing Tqm In Seafood Exporting Firms

Gap analysis is a very useful tool for firms contemplating adoption of a new system. This paper envisages the use of the gap analysis tool as a precursor for Indian seafood exporting firms to adopt total quality management. Total quality management (TQM) is a management philosophy which strives to put quality at the forefront of all its decision-making, thereby satisfying customers. We therefore recommend that firms adopt the TQM system for better alignment of management goals. The gap analysis tool serves as a roadmap for TQM implementation, by showing the management where they actually are and where they want to be. The main gaps were found to be in the areas of usage of SPC tools (66.7%), benchmarking (65.6%), top management commitment (56.25%) and customer focus (48.1%).

Impact of Quality on Ethics and Social Responsibility in Marketing, in Industries in Kerala in the present Indian Scenario

The paper ‘Impact of Quality on Ethics and Social Responsibility in Marketing in Industries in Kerala in the present Indian scenario’ highlights the observations, based on a descriptive research carried out in five leading industries in Kerala, in the private and public sector. Ethics and social responsibilities, practiced in these industries, are reflected in the results of the survey conducted on specific queries like awareness of products/services provided by them, total understanding of the requirements of the customer, open discussion on technical matters, accountability of employees to the society and social needs, consumer ethics vis a vis business ethics etc. Team working goes a long way, in building relations, which in turn, results in a progressive and effective marketing strategy. This assumes paramount importance, considering the severe competition we are facing in the light of liberalization, privatization and globalization, which encompasses the globe. The prediction of India becoming a lead nation, along with USA, China and Japan, in this decade, can get fructified only if we follow a very high standards of ethics and social responsibility, in all domains including marketing. Organizations like TRW.Rane, Sundaram Fasteners, TVS Motors, in Chennai are a few among others in India, who have achieved the highest distinction in quality viz Deming Prize, and these demonstrate their commitment to quality, society and humanity at large. Cost effectiveness, without jeopardizing quality has become the need of the hour and MRTP has become history. This trait is being brought out through the survey and the results speak for themselves. Unethical practices like switch and bait, not only brings shame to the organization, and country but also results in the company getting wiped out from the market. Adherence to standards like ISO 14000 helps to maintain the minimum level of social responsibility and environmental friendliness. Like quality audit, safety audit etc, social audit is being insisted in all progressive countries to ensure that the organization comply with the minimum statutory requirements. The paper also touches upon Corporate Social Responsibility practiced in the industries and this becomes crystal clear through their commitment to improve the community. Green Marketing lays a lot of importance on the three Rs of environmentalism viz Reduce, Reuse and Recycle. The objective of any business is to achieve optimal profit and this is possible only by reducing the cost as well as waste. In this context, management tools like brainstorming, suggestion schemes, benchmarking etc becomes helpful. These characteristics are brought out through the analysis of survey results. The conclusions drawn throw a lot of information on the desirable practices with respect to Ethics and Social Responsibility in Marketing

Performance Study of an Improved Legendre Moment Descriptor as Region-Based Shape Descriptor

This paper reports a novel region-based shape descriptor based on orthogonal Legendre moments. The preprocessing steps for invariance improvement of the proposed Improved Legendre Moment Descriptor (ILMD) are discussed. The performance of the ILMD is compared to the MPEG-7 approved region shape descriptor, angular radial transformation descriptor (ARTD), and the widely used Zernike moment descriptor (ZMD). Set B of the MPEG-7 CE-1 contour database and all the datasets of the MPEG-7 CE-2 region database were used for experimental validation. The average normalized modified retrieval rate (ANMRR) and precision- recall pair were employed for benchmarking the performance of the candidate descriptors. The ILMD has lower ANMRR values than ARTD for most of the datasets, and ARTD has a lower value compared to ZMD. This indicates that overall performance of the ILMD is better than that of ARTD and ZMD. This result is confirmed by the precision-recall test where ILMD was found to have better precision rates for most of the datasets tested. Besides retrieval accuracy, ILMD is more compact than ARTD and ZMD. The descriptor proposed is useful as a generic shape descriptor for content-based image retrieval (CBIR) applications