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Prasanna, K.
- A Novel Approach for Mining High Dimensional Association Rules Using Frequent K-Dimension Set
Authors
1 Department of Computer Science and Engineering, Annamacharya Institute of Technology & Sciences, Rajampet, Andhra Pradesh, IN
2 Department of Computer Science and Engineering, G. Narayanamma Institute of Technology & Sciences for Woman, Hyderabad, Andhra Pradesh, IN
3 Department of Computer Science and Engineering, Annamacharya Institute of Technology & Sciences, Rajampet, IN
Source
Data Mining and Knowledge Engineering, Vol 4, No 7 (2012), Pagination: 337-342Abstract
Association rule mining aims at generating association rules between sets of items in a database. Now a day, due to huge accumulation in the database technology and incredible growth in high dimensional dataset, conventional data base methods are inadequate in extracting useful information. Such large high dimensional data gives rise to a number of new computational challenges not only the increased in number of data objects but also in the increased in number of features/attributes. However, it is becoming very tedious to generate association rules from high dimensional data, because it contains different dimensions or attributes in the large data bases. To improve the high dimensional data mining task, it must be preprocessed efficiently and accurately. In this paper, an Apriori based method for generating association rules from large high dimensional data is proposed. It constitutes 1) Preprocessing and generalizing the data base dimensions; 2) generating high dimensional strong association rules using support and confidence. It can be seen from experiments that the mining algorithm is elegant and efficient, which can obtain more rapid computing speed and sententious rules at the same time It was ascertained that the proposed method is proved to be better in support of generating association rules.Keywords
Association Analysis, Apriori Algorithm, Pre Processing, High Dimensional Data, Support, Confidence, Data Mining.
- Effect of Differential Processing Methods on Elimination of Oligosaccharides in an Underutilized Food and Feed Source, Mucuna Beans
Authors
1 Department of Botany, Government Arts College, Coimbatore - 641 018, Tamil Nadu, IN
2 Department of Biotechnology, Prathyusha Institute of Technology and Management, Aranavayalkuppam, Thiruvallur-602025, Tamil Nadu, IN
Source
Biometrics and Bioinformatics, Vol 4, No 3 (2012), Pagination: 125-129Abstract
Legumes (pulses) contain a wide array of antinutritional factors (ANFs) associated with their nutrients. Suitable scientific and technological processing methods are needed for the elimination of ANFs without affecting the nutritional potential of the pulses. In the present study the selected underutilized pulses [Mucuna monosperma DC wall ex and Mucuna pruriens var. utilis. (Dc wall ex Wight) (Baker ex Burck)] were subjected to differential processing methods to assess their effectiveness in eliminating oligosaccharides which cause flatus in consuming humans. Between the two treatments (soaking followed by cooking and crude α-galactosidase treatment), the crude α-galactosidase enzyme treatment is found to be more effective in eliminating significant levels of oligosaccharides (70-90%).