• KIPS Transactions on Software and Data Engineering
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• v.5 no.3
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• pp.155-164
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• 2016

For frequency pattern analysis of large databases, the new tree-based frequency pattern analysis algorithm which can compensate for the disadvantages of the Apriori method has been variously studied. In frequency pattern tree, the number of nodes is associated with memory allocation, but also affects memory resource consumption and processing speed of the growth. Therefore, reducing the number of nodes in the tree is very important in the frequency pattern mining. However, the absolute criteria which need to order the transaction items for construction frequency pattern tree has lowered the compression ratio of the tree nodes. But most of the frequency based tree construction methods adapted the absolute criteria. FP-tree is typically frequency pattern tree structure which is an extended prefix-tree structure for storing compressed frequent crucial information about frequent patterns. For construction the tree, all the frequent items in different transactions are sorted according to the absolute criteria, frequency descending order. CanTree also need to absolute criteria, canonical order, to construct the tree. In this paper, we proposed a novel frequency pattern tree construction method that does not use the absolute criteria, IRFP-tree algorithm. IRFP-tree(Intersection Rule based FP-tree). IRFP-tree is constituted with the new paradigm of the intersection rule without the use of the absolute criteria. It increased the compression ratio of the tree nodes, and reduced the tree construction time. Our method has the additional advantage that it provides incremental mining. The reported test result demonstrate the applicability and effectiveness of the proposed approach.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8A
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• pp.1265-1272
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• 2000

A data warehouse is able to accommodate efficient data mining query processing and subsequent response by providing information needed for decision making. In such an environment, the data warehouse stores materialized view derived from various sources to enhance query processing. The compensating algorithm to maintain materialized view is well known for a single source site environment. In the compensating algorithm, several problems arise to get results in view maintenance. The problems are due to the overhead in query management within the data warehouse, increased complexity to manage queries in the warehouse as updates occur and increased volume of message traffic between the data source and the warehouse. In this paper, we propose a new algorithm that reduces the overhead in managing queries for new maintenance and that enhances the correctness. We also measured the performance of the new algorithm by evaluating the performance of the existing recomputing view and compensating algorithm and comparing the results with the proposed algorithm.

• Journal of KIISE:Databases
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• v.37 no.4
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• pp.177-184
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• 2010

In these day, many data including sensor, delivery, credit and stock data are generated continuously in massive quantity. It is difficult to learn from these data because they are large in volume and changing fast in their concepts. To handle these problems, learning methods based in sliding window methods over time have been used. But these approaches have a problem of rebuilding models every time new data arrive, which requires a lot of time and cost. Therefore we need very simple incremental learning methods. Bayesian method is an example of these methods but it has a disadvantage which it requries the prior knowledge(probabiltiy) of data. In this study, we propose a learning method based on attribute values. In the proposed method, even though we don't know the prior knowledge(probability) of data, we can apply our new method to data. The main concept of this method is that each attribute value is regarded as an expert learner, summing up the expert learners lead to better results. Experimental results show our learning method learns from data very fast and performs well when compared to current learning methods(decision tree and bayesian).

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.121-139
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• 2014

Predicting corporate failure has been an important topic in accounting and finance. The costs associated with bankruptcy are high, so the accuracy of bankruptcy prediction is greatly important for financial institutions. Lots of researchers have dealt with the topic associated with bankruptcy prediction in the past three decades. The current research attempts to use ensemble models for improving the performance of bankruptcy prediction. Ensemble classification is to combine individually trained classifiers in order to gain more accurate prediction than individual models. Ensemble techniques are shown to be very useful for improving the generalization ability of the classifier. Bagging is the most commonly used methods for constructing ensemble classifiers. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. Instance selection is to select critical instances while deleting and removing irrelevant and harmful instances from the original set. Instance selection and bagging are quite well known in data mining. However, few studies have dealt with the integration of instance selection and bagging. This study proposes an improved bagging ensemble based on instance selection using genetic algorithms (GA) for improving the performance of SVM. GA is an efficient optimization procedure based on the theory of natural selection and evolution. GA uses the idea of survival of the fittest by progressively accepting better solutions to the problems. GA searches by maintaining a population of solutions from which better solutions are created rather than making incremental changes to a single solution to the problem. The initial solution population is generated randomly and evolves into the next generation by genetic operators such as selection, crossover and mutation. The solutions coded by strings are evaluated by the fitness function. The proposed model consists of two phases: GA based Instance Selection and Instance based Bagging. In the first phase, GA is used to select optimal instance subset that is used as input data of bagging model. In this study, the chromosome is encoded as a form of binary string for the instance subset. In this phase, the population size was set to 100 while maximum number of generations was set to 150. We set the crossover rate and mutation rate to 0.7 and 0.1 respectively. We used the prediction accuracy of model as the fitness function of GA. SVM model is trained on training data set using the selected instance subset. The prediction accuracy of SVM model over test data set is used as fitness value in order to avoid overfitting. In the second phase, we used the optimal instance subset selected in the first phase as input data of bagging model. We used SVM model as base classifier for bagging ensemble. The majority voting scheme was used as a combining method in this study. This study applies the proposed model to the bankruptcy prediction problem using a real data set from Korean companies. The research data used in this study contains 1832 externally non-audited firms which filed for bankruptcy (916 cases) and non-bankruptcy (916 cases). Financial ratios categorized as stability, profitability, growth, activity and cash flow were investigated through literature review and basic statistical methods and we selected 8 financial ratios as the final input variables. We separated the whole data into three subsets as training, test and validation data set. In this study, we compared the proposed model with several comparative models including the simple individual SVM model, the simple bagging model and the instance selection based SVM model. The McNemar tests were used to examine whether the proposed model significantly outperforms the other models. The experimental results show that the proposed model outperforms the other models.