• KIPS Transactions on Software and Data Engineering
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• 제2권8호
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• pp.565-570
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• 2013

Previous researches on mining sequential patterns mainly focused on discovering patterns from the point-based event. Interval events with a time interval occur in the real world that have the start and end point. Existing interval pattern mining methods that discover relationships among interval events based on the Allen operators have some problems. These are that interval patterns having three or more interval events can be interpreted as several meanings. In this paper, we propose the I_TPrefixSpan algorithm, which is an efficient sequence pattern mining technique for removing ambiguity in the Interval Patterns Mining. The proposed algorithm generates event sequences that have no ambiguity. Therefore, the size of generated candidate set can be minimized by searching sequential pattern mining entries that exist only in the event sequence. The performance evaluation shows that the proposed method is more efficient than existing methods.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2005년도 춘계학술대회
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• pp.18-24
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• 2005

The methods of data mining are decision tree, association rules, clustering, neural network and so on. Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. We analyze Gyeongnam social indicator survey data by 2003 using association rule technique for environment information. Association rules are useful for determining correlations between attributes of a relation and have applications in marketing, financial and retail sectors. We can use association rule outputs in environmental preservation and environmental improvement.

• Journal of KIISE:Databases
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• 제28권4호
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• pp.577-586
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• 2001

Recently data mining, which is analyzing the stored data and discovering potential knowledge and information in large database is a key research topic in database research data In this paper, we study methods of discovering association rules which are one of data mining techniques. And we propose a technique of discovering association rules using the relative support to consider significant rare data which have the high relative support among some data. And we compare and evaluate existing methods and the proposed method of discovering association rules for discovering significant rare data.

• Science of Emotion and Sensibility
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• 제10권2호
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• pp.147-154
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• 2007

Electrocardiogram(ECG) being the recording of the heart's electrical activity provides valuable clinical information about heart's status. Many researches have been pursued for heart disease diagnosis using ECG so far. However, electrocardio-graph uses foreign diagnosis algorithm in the con due to inaccuracy of domestic diagnosis results for a heart disease. This paper proposes ST-segment extraction technique diagnosing heart disease parameter from raw ECG data. As the ST-segment is used for prediction of Coronary Artery Disease, we can predict heart disease using classification approach in data mining technique. We can also predict patient's clinical characterization from patient clinical data.

• The KIPS Transactions:PartA
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• 제17A권4호
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• pp.181-188
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• 2010

In this study, we suggest the system to analyze network users' action patterns by using Data-Mining Technique. We installed Network Tap to implement the analysis system of network action and copied the network packet. The copied packet is stored at the database through MainMemoryDB(MMDB) of the high-speed. The stored data analyze the users' action patterns by using Data-Mining Technique and then report the results to the network manager on real-time. Also, we applied the standard XML document exchange method to share the data between different systems. We propose this action pattern analysis system operated embedded type of SetToBox to install easily and support low price.

• The Korean Journal of Applied Statistics
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• 제18권3호
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• pp.555-565
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• 2005

To Know customers is very important for the company to survive in its cut-throat competition among coimpetitors. Companies need to manage the relationship with each ana every customer, ant make each of customers as profitable as possible. CRM (Customer relationship management) has emerged as a key solution for managing the profitable relationship. In order to achieve successful CRM customer segmentation is a essential component. Clustering as a data mining technique is very useful to build data-driven segmentation. This paper is concerned with building proper customer segmentation with introducing a credit card company case. Customer segmentation was built based only on transaction data which cattle from customer's activities. Two-step clustering approach which consists of k-means clustering and agglomerative clustering was applied for building a customer segmentation.

• The Journal of Bigdata
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• 제3권1호
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• pp.47-54
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• 2018

Previous studies of business process analysis have analyzed various factors such as task, customer service, operator convenience, and execution time prediction. To accurately analyze these factors, it is effective to utilize actual historical data recorded in information systems. Process mining is a technique for analyzing various elements of a business process from event log data. In this case study, process mining was applied to the transaction data of a purchase agency to analyze the business process of their procurement process, the execution time, and the operators.

• Journal of the Korean Data and Information Science Society
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• 제21권6호
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• pp.1147-1154
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• 2010

One of the important goals in data mining is to discover and decide the relationships between different variables. Association rules are required for this technique and it find meaningful rules by quantifying the relationship between two items based on association measures such as support, confidence, and lift. In this paper, we presented the evaluation criteria of weighted association rule considering item RFM scores as importance of items. Original RFM technique has been used most widely applied method using customer information to find the most profitable customers. And then we compared general association rule technique with weighted association rule technique through the simulation data.

• Health Policy and Management
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• 제16권2호
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• pp.21-48
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• 2006

As prior studies indicate that chronic diseases are mainly attributed to health behavior, preventive health care rather than treatment for illness needs to improve health status. Since chronic conditions require long-term therapy, health care expenditures to treat chronic diseases have been substantial burden at national level. In this point of view, this study suggests that the health promotion program should be based on Knowledge Based System Using Data Mining Technique, we developed a predictive model for preventive healthcare management on diabetes mellitus. Generally, in the outbreak of diabetes mellitus there is a difference in lifestyle and the risk factors according to gender. So we developed a predictive model in accordance with gender difference and applied the Logistic Regression Model based on Data Mining process. The result of the study were as follow. The lift of the last predictive model was an average 2.23 times(male model : 2.13, female model 2.33) more improved than in the random model in upper 10% group. The health risk factors of diabetes mellitus are gender, age, a place of residence, blood pressure, glucose, smoking, drinking, exercise rate. On the basis of these factors, we suggest the program of the health promotion.

• Journal of Korea Spatial Information System Society
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• 제12권1호
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• pp.49-56
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• 2010

Many researches of frequent pattern mining technique for detecting unknown patterns on spatial data have studied actively. Existing data structures have classified into tree-structure and array-structure, and those structures show the weakness of performance on dense or sparse data. Since spatial data have obtained the characteristics of dense and sparse patterns, it is important for us to mine quickly dense and sparse patterns using only single algorithm. In this paper, we propose novel data structure as compressed patricia frequent pattern tree and frequent pattern mining algorithm based on proposed data structure which can detect frequent patterns quickly in terms of both dense and sparse frequent patterns mining. In our experimental result, proposed algorithm proves about 10 times faster than existing FP-Growth algorithm on both dense and sparse data.