X-ray absorption spectroscopy characterization of iron-oxide nanoparticles synthesized by high temperature plasma processing

Iron-oxide nanoparticles have been synthesized by high temperature arc plasma route with different plasma currents and characterized for their structure, morphology and local atomic order. Fe K-edge x-ray absorption spectra reveal distinct local structure of the samples grown with different plasma currents. We have shown that the local disorder is higher for the higher plasma current grown samples that also have a larger average particle-size. The results provide useful information to control structural and morphological properties of nanoparticles grown by high temperature plasma synthesis process.

Similarity Data Item Set Approach: An Encoded Temporal Data Base Technique

Data mining has been widely recognized as a powerful tool to explore added value from large-scale databases. Finding frequent item sets in databases is a crucial in data mining process of extracting association rules. Many algorithms were developed to find the frequent item sets. This paper presents a summary and a comparative study of the available FP-growth algorithm variations produced for mining frequent item sets showing their capabilities and efficiency in terms of time and memory consumption on association rule mining by taking application of specific information into account. It proposes pattern growth mining paradigm based FP-tree growth algorithm, which employs a tree structure to compress the database. The performance study shows that the anti- FP-growth method is efficient and scalable for mining both long and short frequent patterns and is about an order of magnitude faster than the Apriority algorithm and also faster than some recently reported new frequent-pattern mining.

An Application of Bayesian classification to Interval Encoded Temporal mining with prioritized items

In real life, media information has time attributes either implicitly or explicitly known as temporal data. This paper investigates the usefulness of applying Bayesian classification to an interval encoded temporal database with prioritized items. The proposed method performs temporal mining by encoding the database with weighted items which prioritizes the items according to their importance from the user perspective. Naive Bayesian classification helps in making the resulting temporal rules more effective. The proposed priority based temporal mining (PBTM) method added with classification aids in solving problems in a well informed and systematic manner. The experimental results are obtained from the complaints database of the telecommunications system, which shows the feasibility of this method of classification based temporal mining.