• International Journal of Contents
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• v.3 no.2
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• pp.18-24
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• 2007

Biological sequences such as DNA and amino acid sequences typically contain a large number of items. They have contiguous sequences that ordinarily consist of more than hundreds of frequent items. In biological sequences analysis(BSA), a frequent contiguous sequence search is one of the most important operations. Many studies have been done for mining sequential patterns efficiently. Most of the existing methods for mining sequential patterns are based on the Apriori algorithm. In particular, the prefixSpan algorithm is one of the most efficient sequential pattern mining schemes based on the Apriori algorithm. However, since the algorithm expands the sequential patterns from frequent patterns with length-1, it is not suitable for biological datasets with long frequent contiguous sequences. In recent years, the MacosVSpan algorithm was proposed based on the idea of the prefixSpan algorithm to significantly reduce its recursive process. However, the algorithm is still inefficient for mining frequent contiguous sequences from long biological data sequences. In this paper, we propose an efficient method to mine maximal frequent contiguous sequences in large biological data sequences by constructing the spanning tree with a fixed length. To verify the superiority of the proposed method, we perform experiments in various environments. The experiments show that the proposed method is much more efficient than MacosVSpan in terms of retrieval performance.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.278-283
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• 2006

결정 트리는 큰 가설 공간을 가지고 있어 유연하고 강인한 성능을 지닐 수 있다. 하지만 결정트리가 학습 데이터에 지나치게 적응되는 경향이 있다. 학습데이터에 과도하게 적응되는 경향을 없애기 위해 몇몇 가지치기 알고리즘이 개발되었다. 하지만, 데이터가 속성 축에 평행하지 않아서 오는 공간 낭비의 문제는 이러한 방법으로 해결할 수 없다. 따라서 본 논문에서는 다변수 노드를 사용한 선형 분류기를 이용하여 이러한 문제점을 해결하는 방법을 제시하였으며, 결정트리의 성능을 높이고자 지지 벡터 머신을 도입하였다(SVMDT). 본 논문에서 제시한 알고리즘은 세 가지 부분으로 이루어졌다. 첫째로, 각 노드에서 사용할 속성을 선택하는 부분과 둘째로, ID3를 이 목적에 맞게 바꾼 알고리즘과 마지막으로 기본적인 형태의 가지치기 알고리즘을 개발하였다. UCI 데이터 셋을 이용하여 OC1, C4.5, SVM과 비교한 결과, SVMDT는 개선된 결과를 보였다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.2
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• pp.28-37
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• 2011

With growing concerns about healthy life recently, although the podiatry which deals with the whole area for diagnosis, treatment of foot and leg, and prevention has been widely interested, research in our country is not active. Also, because most of the previous researches in data analysis performed the quantitative approaches, the reasonable level of reliability for clinical application could not be guaranteed. Clinical data mining utilizes various data mining analysis methods for clinical data, which provides decision support for expert's diagnosis and treatment for the patients. Because the decision tree can provide good explanation and description for the analysis procedure and is easy to interpret the results, it is simple to apply for clinical problems. This study investigate rules of item of diagnosis in disease types for adapting decision tree after collecting diagnosed data patients who are 2620 feet of 1310(males:633, females:677) in shoes clinic (department of rehabilitation medicine, Chungnam National University Hospital). and we classified 15 foot diseases followed factor of 22 foot diseases, which investigated diagnosis of 64 rules. Also, we analyzed and compared correlation relationship of characteristic of disease and factor in types through made decision tree from 5 class types(infants, child, adolescent, adult, total). Investigated results can be used qualitative and useful knowledge for clinical expert`s, also can be used tool for taking effective and accurate diagnosis.

• Journal of The Korean Association of Information Education
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• v.17 no.4
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• pp.433-447
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• 2013

The purpose of this study is to find out the factors of the students' achievement on the computer training courses which are based on computer practice. In order to improve the academic achievement of the students, it is necessary to analyze the factors affecting academic achievement and apply the results of the analysis to education. In particular, it is necessary to study for finding out factors of the academic achievement in practical computer training courses, because these courses are different from other courses focusing on the theory. In this study, in order to find out the factors, the logistic regression analysis and the decision tree analysis which is the field of data mining were peformed. For the experimental data, the test results of the MOS certification of the S university in seoul were used. Through logistic regression analysis it is found that the factors of the professors, class size, lecture time, group(lecture period) are important in order. Through decision tree analysis of data mining, it is found that there are some additional factors ; entrance year, whether the course is retaken, and the classroom environment. and these various factors effect the academic achievement compositively as identified through the model tree. The tree model was presented as a result of the analysis, and the importance of the factors is expressed numerically from multiple tree models by using the proposed mathematical formula.

• The KIPS Transactions:PartD
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• v.13D no.5 s.108
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• pp.651-660
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• 2006

Algorithms for mining association rules based on the Apriori algorithm use the hash tree data structure for storing and counting supports of the candidate frequent itemsets and the most part of the execution time is consumed for searching in the hash tree. The DHP(Direct Hashing and Pruning) algorithm makes efforts to reduce the number of the candidate frequent itemsets to save searching time in the hash tree. For this purpose, the DHP algorithm does preparative simple counting supports of the candidate frequent itemsets. At this time, the DHP algorithm uses the direct hash table to reduce the overhead of the preparative counting supports. This paper proposes and evaluates an efficient hashing mechanism for the direct hash table $H_2$ which is for pruning in phase 2 and the hash tree $C_k$, which is for counting supports of the candidate frequent itemsets in all phases. The results showed that the performance improvement due to the proposed hashing mechanism was 82.2% on the maximum and 18.5% on the average compared to the conventional method using a simple mod operation.

• The KIPS Transactions:PartD
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• v.15D no.6
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• pp.741-754
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• 2008

Due to the characteristics of a data stream, it is very important to confine the memory usage of a data mining process regardless of the amount of information generated in the data stream. For this purpose, this paper proposes the Prime pattern tree(PPT) for finding frequent itemsets over data streams with using the confined memory space. Unlike a prefix tree, a node of a PPT can maintain the information necessary to estimate the current supports of several itemsets together. The length of items in a prime pattern can be reduced the total number of nodes and controlled by split_delta $S_{\delta}$. The size and the accuracy of the PPT is determined by $S_{\delta}$. The accuracy is better as the value of $S_{\delta}$ is smaller since the value of $S_{\delta}$ is large, many itemsets are estimated their frequencies. So it is important to consider trade-off between the size of a PPT and the accuracy of the mining result. Based on this characteristic, the size and the accuracy of the PPT can be flexibly controlled by merging or splitting nodes in a mining process. For finding all frequent itemsets over the data stream, this paper proposes a PPT to replace the role of a prefix tree in the estDec method which was proposed as a previous work. It is efficient to optimize the memory usage for finding frequent itemsets over a data stream in confined memory space. Finally, the performance of the proposed method is analyzed by a series of experiments to identify its various characteristics.

• Journal of KIISE:Software and Applications
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• v.34 no.11
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• pp.984-990
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• 2007

As the amount of electronic documents increases explosively, automatic text categorization methods are needed to identify those of interest. Most methods use machine learning techniques based on a word set. This paper introduces a new method, called FPTC (FP-Tree based Text Classifier). FP-Tree is a data structure used in data-mining. In this paper, a method of storing text sentence patterns in the FP-Tree structure and classifying text using the patterns is presented. In the experiments conducted, we use our algorithm with a #Mutual Information and Entropy# approach to improve performance. We also present an analysis of the algorithm via an ordinary differential categorization method.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.100-102
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• 2002

데이터 마이닝의 대표적인 기법인 연관규칙 마이닝을 위한 다양만 알고리즘들이 제안되었고, 각 알고리즘에 따른 대용량 데이터에 대한 신속한 탐색을 위한 독특한 자료구조가 제안되었다 각 자료구조의 특성에 따른 알고리즘 성능은 데이터의 패턴에 크게 의존한다. 본 논문에서는 Concept을 형성하는 세가지 대표적인 자료구조인 Hash Tree, Lattice. FP-Tree에 대해 비교 분석해보고, 데이터 패턴에 적합한 효율적인 알고리즘의 설계 위한 framework을 제안한다.

• Journal of Korean Society of Environmental Engineers
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• v.38 no.2
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• pp.87-95
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• 2016

Turbidity is a key indicator to the user that the 'Discolored Water' phenomenon known to be caused by corrosion of the pipeline in the water supply system. 'Discolored Water' is defined as a state with a turbidity of the degree to which the user visually be able to recognize water. Therefore, this study used data mining techniques in order to estimate turbidity changes in water supply system. Decision tree analysis was applied in data mining techniques to develop estimation models for turbidity changes in the water supply system. The pH and residual chlorine dataset was used as variables of the turbidity estimation model. As a result, the case of applying both variables(pH and residual chlorine) were shown more reasonable estimation results than models only using each variable. However, the estimation model developed in this study were shown to have underestimated predictions for the peak observed values. To overcome this disadvantage, a high-pass filter method was introduced as a pretreatment of estimation model. Modified model using high-pass filter method showed more exactly predictions for the peak observed values as well as improved prediction performance than the conventional model.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.1
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• pp.97-101
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• 2012

Objective: With the background of aging population in China and advances in clinical medicine, the amount of operations on old patients increases correspondingly, which imposes increasing challenges to critical care medicine and geriatrics. The study was designed to describe information on the length of ICU stay from a single institution experience of old critically ill gastric cancer patients after surgery and the framework of incorporating data-mining techniques into the prediction. Methods: A retrospective design was adopted to collect the consecutive data about patients aged 60 or over with a gastric cancer diagnosis after surgery in an adult intensive care unit in a medical university hospital in Shenyang, China, from January 2010 to March 2011. Characteristics of patients and the length their ICU stay were gathered for analysis by univariate and multivariate Cox regression to examine the relationship with potential candidate factors. A regression tree was constructed to predict the length of ICU stay and explore the important indicators. Results: Multivariate Cox analysis found that shock and nutrition support need were statistically significant risk factors for prolonged length of ICU stay. Altogether, eight variables entered the regression model, including age, APACHE II score, SOFA score, shock, respiratory system dysfunction, circulation system dysfunction, diabetes and nutrition support need. The regression tree indicated comorbidity of two or more kinds of shock as the most important factor for prolonged length of ICU stay in the studied sample. Conclusions: Comorbidity of two or more kinds of shock is the most important factor of length of ICU stay in the studied sample. Since there are differences of ICU patient characteristics between wards and hospitals, consideration of the data-mining technique should be given by the intensivists as a length of ICU stay prediction tool.