• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.5 s.311
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• pp.17-28
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• 2006

In this paper, we investigate a problem of updating sequential patterns when new transactions are added to a database. We present an efficient updating algorithm for sequential pattern mining that incrementally updates added transactions by reusing frequent patterns found previously. Our performance study shows that this method outperforms both AprioriAll and PrefixSpan algorithm which updates from scratch, since our method can efficiently utilize reduced candidate sets which result from the incremental updating technique.

• Journal of the Korea Society of Computer and Information
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• v.18 no.11
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• pp.125-136
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• 2013

In this paper, we proposed SSL VPN-based network access control technology which can verify user authentication and integrity of user terminal. Using this technology, user can carry out a safety test to check security services such as security patch and virus vaccine for user authentication and user terminal, during the VPN-based access to an internal network. Moreover, this system protects a system from external security threats, by detecting malicious codes, based on behavioral patterns from user terminal's window API information, and comparing the similarity of sequential patterns to improve the reliability of detection.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.5
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• pp.896-904
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• 1987

In this paper, a design method for sequential circuit which is easy to have Built-in Self Test is kproposed using the functional advantages of multifunctional BILBO and LSSD. To achieve the hardware reduction, it is designed that a multifunctional BILBO has double operational functions of NLFSR and LFSR, when neccessary, and that test signal could be used as an input-output signal in the same line. By applying the proposed multifunctional BILBO to the sequential PLA, the test patterns and the additional circuit could be reduced in test operation and the propagation delay is vanished in normal operation, as we expected. Above them, the partitioned method for large scale sequential circuit is also suggested and it is observed that test patterns and additional circuit in them reduced by this method.

• The Research Journal of the Costume Culture
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• v.31 no.5
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• pp.607-626
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• 2023

This study analyzes consumer fashion purchase patterns from a big data perspective. Transaction data from 1 million transactions at two Korean fashion brands were collected. To analyze the data, R, Python, the SPADE algorithm, and network analysis were used. Various consumer purchase patterns, including overall purchase patterns, seasonal purchase patterns, and age-specific purchase patterns, were analyzed. Overall pattern analysis found that a continuous purchase pattern was formed around the brands' popular items such as t-shirts and blouses. Network analysis also showed that t-shirts and blouses were highly centralized items. This suggests that there are items that make consumers loyal to a brand rather than the cachet of the brand name itself. These results help us better understand the process of brand equity construction. Additionally, buying patterns varied by season, and more items were purchased in a single shopping trip during the spring season compared to other seasons. Consumer age also affected purchase patterns; findings showed an increase in purchasing the same item repeatedly as age increased. This likely reflects the difference in purchasing power according to age, and it suggests that the decision-making process for pur- chasing products simplifies as age increases. These findings offer insight for fashion companies' establishment of item-specific marketing strategies.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.2
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• pp.137-144
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• 2013

A sequential pattern mining is finding out frequent patterns from the data set in time order. In this field, a dynamic weighted sequential pattern mining is applied to a computing environment that changes depending on the time and it can be utilized in a variety of environments applying changes of dynamic weight. In this paper, we propose a new sequence data mining method to explore the stream data by applying the dynamic weight. This method reduces the candidate patterns that must be navigated by using the dynamic weight according to the relative time sequence, and it can find out frequent sequence patterns quickly as the data input and output using a hash structure. Using this method reduces the memory usage and processing time more than applying the existing methods. We show the importance of dynamic weighted mining through the comparison of different weighting sequential pattern mining techniques.

• Journal of Korean Academy of Nursing
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• v.37 no.1
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• pp.145-155
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• 2007

Purpose: The purpose of this study was to describe and to analyze real communication about a patient's discomfort between a patient with cancer and a nurse. Method: A dialogue analysis method was utilized. Fifteen patients and 4 nurses who participated in this research gave permission to be videotaped. The data was collected from January, 3 to February 28, 2006. Results: The communication process consisted of 4 functional stages: 'introduction stage', 'assessment stage', 'intervention stage' and 'final stage'. After trying to analyze pattern reconstruction in the 'assessment stage' and 'intervention stage', sequential patterns were identified. In the assessment stage, if the nurse lead the communication, the sequential pattern was 'assessment question-answer' and if the patient lead the communication, it was 'complaint-response'. In the intervention stage, the sequential pattern was 'nursing intervention-acceptance'. Conclusion: This research suggests conversation patterns between patients with cancer and nurses. Therefore, this study will provide insight for nurses in cancer units by better understanding communication behaviors.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.45
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• pp.101-112
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• 1998

Data mining, which is also referred to as knowledge discovery in database, means a process of nontrivial extraction of implicit, previously unknown and potentially useful information (such as knowledge rules, constraints, regularities) from data in databases. The discovered knowledge can be applied to information management, decision making, and many other applications. In this paper, a new data mining problem, discovering sequential patterns, is proposed which is to find all sequential patterns using sampling method. Recognizing that the quantity of database is growing exponentially and transaction database is frequently updated, sampling method is a fast algorithm reducing time and cost while extracting the trend of customer behavior. This method analyzes the fraction of database but can in general lead to results of a very high degree of accuracy. The relaxation factor, as well as the sample size, can be properly adjusted so as to improve the result accuracy while minimizing the corresponding execution time. The superiority of the proposed algorithm will be shown through analyzing accuracy and efficiency by comparing with Apriori All algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.169-189
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• 2015

Detection of anomalous events from video streams is a challenging problem in many video surveillance applications. One such application that has received significant attention from the computer vision community is traffic video surveillance. In this paper, a Lossy Count based Sequential Temporal Pattern mining approach (LC-STP) is proposed for detecting spatio-temporal abnormal events (such as a traffic violation at junction) from sequences of video streams. The proposed approach relies mainly on spatial abstractions of each object, mining frequent temporal patterns in a sequence of video frames to form a regular temporal pattern. In order to detect each object in every frame, the input video is first pre-processed by applying Gaussian Mixture Models. After the detection of foreground objects, the tracking is carried out using block motion estimation by the three-step search method. The primitive events of the object are represented by assigning spatial and temporal symbols corresponding to their location and time information. These primitive events are analyzed to form a temporal pattern in a sequence of video frames, representing temporal relation between various object's primitive events. This is repeated for each window of sequences, and the support for temporal sequence is obtained based on LC-STP to discover regular patterns of normal events. Events deviating from these patterns are identified as anomalies. Unlike the traditional frequent item set mining methods, the proposed method generates maximal frequent patterns without candidate generation. Furthermore, experimental results show that the proposed method performs well and can detect video anomalies in real traffic video data.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.8
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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.

• Journal of the Korean Institute of Telematics and Electronics
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• v.13 no.3
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• pp.1-6
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• 1976

In this paper, a method of test pattern generation for the functional failure in both combinational and sequentlal logic networks by using exterded Boole an difference is proposed. The proposed technique provides a systematic approach for the test pattern generation procedure by computing Boolean difference of the Boolean function that represents the Logic network for which the test patterns are to be generated. The computer experimental results show that the proposed method is suitable for both combinational and asynchronous sequential logic networks. Suitable models of clocked flip flops may make it possible for one to extend this method to synchronous sequential logic networks.