A Novel Approach to Extract High Utility Itemsets from Distributed Databases

Traditional approaches in data mining focus on support and confidence measures which are just statistics based. Support and confidence measures which are based on the frequency count of the items enable us to derive the frequent itemsets. The frequency of the items as a single factor does not represent the interestingness of the items. To enhance the process of data mining tasks based on the value of the product, several researches were conducted. It resulted in utility mining which is an emerging field of research in data mining. In the recent years various data mining approaches have been implemented in order to find the high utility itemsets. The main objective of utility mining is to identify the itemsets with highest utilities, by considering the subjectively defined utility values, as set by the user. Existing methods based on utility mining concept focus on centralized systems where the data and associated processing is pertained to a particular location. As a further step ahead we try to implement the utility mining concept in a distributed environment. In this approach we use a sophisticated way of mining high utility itemsets using a Fast Utility Mining (FUM) algorithm

Haematological parameters of Cyprinus carpio with reference to probiotic feed: A machine learning approach

The study aims to analyze the haematological parameters of Cyprinus carpio with reference to the formulation of the probiotic fortified feeds using a machine learning approach. C. carpio fed with pelletized feed, probiotic pelletized feed (5% Lysinibacillus macroides), probiotic pearl beads (5% L. macroides) and probiotic rice puff (5% L. macroides) for 60 days. At the end of the experiments, using blood samples, the haematological indices such as leucocytes, erythrocytes, hemoglobin, hematocrit and packed-cell-volume, were analyzed. Duncan’s Multiple Range Test showed that the haematological parameters in control feeding regimes significantly (P<0.05) were low compared with that of the probiotic feeding regimes. The data sets of different feeding regimes were classified using the machine learning method. In the present study, the classifiers like the Random Forest, the Linear Model, and the Decision Tree were employed. To identify the relationship between the features, correlation coefficient and dendrogram were applied. The results of the machine learning method showed high accuracy (98%) in random forest methods followed by the decision tree method. The correlation coefficient between the haematological indices recorded a positive value. But, calculated values of mean corpuscular volume, mean corpuscular hemoglobin and mean corpuscular haemoglobin concentration were either low positive or negatively correlated with other haematological indices. Based on the results, the Random Forest, Linear Model and Decision Tree Analysis might be considered for haematological classification of the fish haematological data set