Set Representation for Rule Generation Algorithms

The task of mining the association rule has become one of the most widely used discovery pattern methods in Knowledge Discovery in Databases (KDD). One such task is to represent the itemset in the memory. The representation of the itemset largely depend on the type of data structure that is used for storing them. Computing the process of mining the association rule im- pacts the memory and time requirement of the itemset. With the increase in the dimensionality of data and datasets, mining such large volume of datasets will be difficult since all these itemsets cannot be placed in the main memory. As representation of an itemset greatly affects the efficiency of the rule mining association, a compact and compress representation of an itemset is needed. In this paper, a set representation is introduced which is more memory and cost efficient. Bitmap representation takes one byte for an element but the set representation uses one bit. The set representation is being incorporated in Apriori Algorithm. Set representation is also being tested for different rule generation algorithms. The complexities of these different rule generation algorithms using set representation are being compared in terms of memory and time execution

Set representation for rule-generation algorithms

The task of mining association rules has become one of the most widely used discovery pattern methods in knowledge discovery in databases (KDD). One such task is to represent an item set in the memory. The representation of the item set largely depends on the type of data structure that is used for storing them. Computing the process of mining an association rule impacts the memory and time requirements of the item set. With the constant increase of the dimensionality of data and data sets, mining such a large volume of data sets will be difficult since all of these item sets cannot be placed in the main memory. As the representation of an item set greatly affects the efficiency of the rule-mining association, a compact and compressed representation of the item set is needed. In this paper, a set representation is introduced that is more memory- and cost-efficient. Bitmap representation takes 1 byte for an element, but a set representation uses 1 bit. The set representation is being incorporated in the Apriori algorithm. Set representation is also being tested for different rule-generation algorithms. The complexities of these different rule-generation algorithms that use set representation are being compared in terms of memory and time of execution

Long-term management of rice agroecosystem towards climate change mitigation

The Intergovernmental Panel on Climate Change recognize to agriculture the responsibility for about 15 % of global anthropogenic greenhouse gas (GHG) emissions, contributing to global warming. The increasing nutrient inputs in industrial agriculture affect the GHG concentration in the atmosphere and varies substantially due to rate and type of fertilizers applied to the crops, making the management more or less sustainable. In this perspective, this study has investigated at small scale the effect of different adjusted agricultural management practices, based on different nutrient dosage, to optimize the effect of rice cropping systems on carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) emissions. Farmyard manure (FYM), rice stubbles and Azolla integrated with chemical fertilizers have been correlated with microbial and enzymatic activities, and with different carbon and nitrogen fractions in acid Inceptisol. Results have revealed that the integrated nutrient management used in rice-rice agroecosystem yielded a peak for CO2 and CH4 emissions, whereas two peaks for N2O emission. This study has shown an increase in greenhouse gas emission intensity (GHGI) and grain yield of rice in the following order: rice stubbles > FYM > Azolla and it has confirmed CH4 emission as the dominant contributor to GHGI from the rice-rice agroecosystem. When analyzed together GHGs emission and soil properties, a positive correlation was found with biological properties as well as with the different carbon and nitrogen fractions in soil. The highest GHGI has been highlighted in the treatment where recommended dose of chemical fertilizers has been combined with rice stubbles, primarily due to the increase in CH4 emissions. In contrast, the lowest GHGI has been exhibited in Azolla treated plot, probably due to the cumulative effects of the photosynthetic rate of Azolla, the release of oxygen from the Azolla roots, and the physical protection capacity of the Azolla cover, which partially avoid the CH4 diffusion from the standing water. The seasonality did not affect the estimated rates of GHGI that have been lower both in case of winter and autumn rice compared to previous studies, probably for dissimilarities in management practices. Further research is required in other cropping sequences for addressing the ecological contribution of smallholder agriculture to help reducing GHG emissions, thus, mitigating global warming with actions at local scale