Nagender Parimi

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Frequent Pattern Mining (FPM) is a very powerful paradigm which encompasses an entire class of data mining tasks. The specific tasks encompassed by FPM include the mining of increasingly complex and informative patterns, in complex structured and unstructured relational datasets, such as: Itemsets or co-occurrences [1] (transactional, unordered data),(More)
Decision trees have been found very effective for classification especially in Data Mining. This paper aims at improving the performance of the SLIQ decision tree algorithm (Mehta et. al,1996) for classification in data mining The drawback of this algorithm is that large number of gini indices have to be computed at each node of the decision tree. In order(More)
Frequent Pattern Mining (FPM) is a very powerful paradigm for mining informative and useful patterns in massive, complex datasets. In this paper we propose the Data Mining Template Library, a collection of generic containers and algorithms for data mining, as well as persistency and database management classes. DMTL provides a systematic solution to a whole(More)
Frequent Pattern Mining (FPM) is a very powerful paradigm for mining informative and useful patterns in massive, complex datasets. In this paper we propose the Data Mining Template Library, a collection of generic containers and algorithms for data mining, as well as persistency and database management classes. DMTL provides a systematic solution to a whole(More)
Efficient adaptive Internet video streaming of scalable video requires suitable transport control algorithms to adapt the transmission rate to the fluctuating network conditions. The algorithms implemented in TCP, the most prevalent Internet transport control protocol, are not suitable for video streaming as they create undesirable rate and hence viewing(More)
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