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

Large enhanced dielectric permittivity in polyaniline passivated core-shell nano magnetic iron oxide by plasma polymerization

Commercial samples of Magnetite with size ranging from 25–30nm were coated with polyaniline by using radio frequency plasma polymerization to achieve a core shell structure of magnetic nanoparticle (core)–Polyaniline (shell). High resolution transmission electron microscopy images confirm the core shell architecture of polyaniline coated iron oxide. The dielectric properties of the material were studied before and after plasma treatment. The polymer coated magnetite particles exhibited a large dielectric permittivity with respect to uncoated samples. The dielectric behavior was modeled using a Maxwell–Wagner capacitor model. A plausible mechanism for the enhancement of dielectric permittivity is proposed

Cure Characteristics and Mechanical Properties of Rubber Ferrite Composites Based on Nano-Nickel Ferrites

Ultra fine nickel ferrite have been synthesized by the sol-gel method. By heat treating different portions of the prepared powder separately at different temperatures, nano-sized particles of nickel ferrite with varying particle sizes were obtained. These powders were characterised by the X-ray diffraction and then incorporated in the nitrile rubber matrix according to a specific recipe for various loadings. The cure characteristics and the mechanical properties of these rubber ferrite composites (RFCs) were evaluated. The effect of loading and the grain size of the filler on the cure characteristics and tensile properties were also evaluated. It is found that the grain size and porosity of the filler plays a vital role in determining the mechanical properties of the RFCs

Formation of carbon-encapsulated metallic nano-particles from metal acetylides by electron beam irradiation

Transition metal acetylides, MC2 (M=Fe, Co and Ni), exhibit ferromagnetic behavior of which TC is characteristic of their size and structure. CoC2 synthesized in anhydrous condition exhibited cubic structure with disordered C2− 2 orientation. Once being exposed to water (or air), the particles behave ferromagnetically due to the lengthening of the Co–Co distance by the coordination of water molecules to Co2+ cations. Heating of these particles induces segregation of metallic cores with carbon mantles. Electron beam or 193 nm laser beam can produce nanoparticles with metallic cores covered with carbon mantles

Modification of Polypropylene/Polystyrene blend using Nanoclay and Sisal Cellulose nano fiber

Cochin University of Science and Technology

Evolving Classifiers - Evolutionary Algorithms in Data Mining

Data mining means to summarize information from large amounts of raw data. It is one of the key technologies in many areas of economy, science, administration and the internet. In this report we introduce an approach for utilizing evolutionary algorithms to breed fuzzy classifier systems. This approach was exercised as part of a structured procedure by the students Achler, Göb and Voigtmann as contribution to the 2006 Data-Mining-Cup contest, yielding encouragingly positive results.

Fast computation of concept lattices using data mining techniques

We present a new algorithm called TITANIC for computing concept lattices. It is based on data mining techniques for computing frequent itemsets. The algorithm is experimentally evaluated and compared with B. Ganter's Next-Closure algorithm.

Mining minimal non-redundant association rules using frequent closed itemsets

The problem of the relevance and the usefulness of extracted association rules is of primary importance because, in the majority of cases, real-life databases lead to several thousands association rules with high confidence and among which are many redundancies. Using the closure of the Galois connection, we define two new bases for association rules which union is a generating set for all valid association rules with support and confidence. These bases are characterized using frequent closed itemsets and their generators; they consist of the non-redundant exact and approximate association rules having minimal antecedents and maximal consequences, i.e. the most relevant association rules. Algorithms for extracting these bases are presented and results of experiments carried out on real-life databases show that the proposed bases are useful, and that their generation is not time consuming.

Towards semantic web mining

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. The idea is to improve, on the one hand, the results of Web Mining by exploiting the new semantic structures in the Web; and to make use of Web Mining, on overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable.

Efficient data mining based on formal concept analysis

Formal Concept Analysis is an unsupervised learning technique for conceptual clustering. We introduce the notion of iceberg concept lattices and show their use in Knowledge Discovery in Databases (KDD). Iceberg lattices are designed for analyzing very large databases. In particular they serve as a condensed representation of frequent patterns as known from association rule mining. In order to show the interplay between Formal Concept Analysis and association rule mining, we discuss the algorithm TITANIC. We show that iceberg concept lattices are a starting point for computing condensed sets of association rules without loss of information, and are a visualization method for the resulting rules.

Semantic web mining and the representation, analysis, and evolution of web space

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. This survey analyzes the convergence of trends from both areas: Growing numbers of researchers work on improving the results of Web Mining by exploiting semantic structures in the Web, and they use Web Mining techniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic Web itself. The second aim of this paper is to use these concepts to circumscribe what Web space is, what it represents and how it can be represented and analyzed. This is used to sketch the role that Semantic Web Mining and the software agents and human agents involved in it can play in the evolution of Web space.

Mining association rules in folksonomies

Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. These systems provide currently relatively few structure. We discuss in this paper, how association rule mining can be adopted to analyze and structure folksonomies, and how the results can be used for ontology learning and supporting emergent semantics. We demonstrate our approach on a large scale dataset stemming from an online system.