• Proceedings of the KSRS Conference
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• v.2
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• pp.627-630
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• 2006

Sequential Pattern Mining is the mining approach which addresses the problem of discovering the existent maximal frequent sequences in a given databases. In the daily and scientific life, sequential data are available and used everywhere based on their representative forms as text, weather data, satellite data streams, business transactions, telecommunications records, experimental runs, DNA sequences, histories of medical records, etc. Discovering sequential patterns can assist user or scientist on predicting coming activities, interpreting recurring phenomena or extracting similarities. For the sake of that purpose, the core of sequential pattern mining is finding the frequent sequence which is contained frequently in all data sequences. Beside the discovery of frequent itemsets, sequential pattern mining requires the arrangement of those itemsets in sequences and the discovery of which of those are frequent. So before mining sequences, the main task is checking if one sequence is a subsequence of another sequence in the database. In this paper, we implement the subsequence matching method as the preprocessing step for sequential pattern mining. Matched sequences in our implementation are the normalized sequences as the form of number chain. The result which is given by this method is the review of matching information between input mapped sequences.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.457-462
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• 2006

Sequential Pattern Mining is the mining approach which addresses the problem of discovering the existent maximal frequent sequences in a given databases. In the daily and scientific life, sequential data are available and used everywhere based on their representative forms as text, weather data, satellite data streams, business transactions, telecommunications records, experimental runs, DNA sequences, histories of medical records, etc. Discovering sequential patterns can assist user or scientist on predicting coming activities, interpreting recurring phenomena or extracting similarities. For the sake of that purpose, the core of sequential pattern mining is finding the frequent sequence which is contained frequently in all data sequences. Beside the discovery of frequent itemsets, sequential pattern mining requires the arrangement of those itemsets in sequences and the discovery of which of those are frequent. So before mining sequences, the main task is checking if one sequence is a subsequence of another sequence in the database. In this paper, we implement the subsequence matching method as the preprocessing step for sequential pattern mining. Matched sequences in our implementation are the normalized sequences as the form of number chain. The result which is given by this method is the review of matching information between input mapped sequences.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.599-617
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• 2006

Recently many LBS(Location Based Service) systems are issued in mobile computing systems. Spatial-Temporal Moving Sequence Pattern Mining is a new mining method that mines user moving patterns from user moving path histories in a sensor network environment. The frequent pattern mining is related to the items which customers buy. But on the other hand, our mining method concerns users' moving sequence paths. In this paper, we consider the sequence of moving paths so we handle the repetition of moving paths. Also, we consider the duration that user spends on the location. We proposed new Apriori_msp based on the Apriori algorithm and evaluated its performance results.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.281-287
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• 2021

Bioinformatics is formed with a blend of biology and informatics technologies and it employs the statistical methods and approaches for attending the concerning issues in the domains of nutrition, medical research and towards reviewing the living environment. The ceaseless growth of DNA sequencing technologies has resulted in the production of voluminous genomic data especially the DNA sequences thus calling out for increased storage and bandwidth. As of now, the bioinformatics confronts the major hurdle of management, interpretation and accurately preserving of this hefty information. Compression tends to be a beacon of hope towards resolving the aforementioned issues. Keeping the storage efficiently, a methodology has been recommended which for attending the same. In addition, there is introduction of a competent algorithm that aids in exact matching of small pattern. The DNA representation sequence is then implemented subsequently for determining 2 bases to 6 bases matching with the remaining input sequence. This process involves transforming of DNA sequence into an ASCII symbols in the first level and compress by using LZ77 compression method in the second level and after that form the grid variables with size 3 to hold the 100 characters. In the third level of compression, the compressed output is in the grid variables. Hence, the proposed algorithm S_Pattern DNA gives an average better compression ratio of 93% when compared to the existing compression algorithms for the datasets from the UCI repository.

• Korea journal of population studies
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• v.25 no.2
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• pp.107-138
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• 2002

In general, women's labor force participation follows a M-curve pattern because women's state of economic activity usually changes by their life course stage. This research attentions that the effect of sequence of life course as well as the effects of‘marriage bar’, or‘maternity leave’is very important in understanding women's chaning economic activity status. First, this research hypothesizes that women's four patterns of job career such as‘continuous pattern’,‘discontinuous pattern’,‘non-economic activity pattern’,‘marriage leave pattern’result a significant difference in social and demographic variables. Second, this research analyzes the effect of ordering and timing of life events on women's work transition. This research investigates labor market dynamics to conceptualize labor market behaviors using longitudinal data and sequence analysis and event history analysis. We find that four patterns of job career vary by age, educational attainment, having a certificate or not, their parents’human capital and health status. And we find that the ordering and timing of‘participation in labor market’and‘marriage’determine the pattern of women's work transition.

• Journal of Korea Multimedia Society
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• v.20 no.5
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• pp.800-807
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• 2017

The Pattern Sequence-based Forecasting (PSF) is an approach to forecast the behavior of time series based on similar pattern sequences. The innovation of PSF method is to convert the load time series into a label sequence by clustering technique in order to lighten computational burden. However, it brings about a new problem in determining the number of clusters and it is subject to insufficient similar days occasionally. In this paper we proposed an adaption of the PSF method, which introduces a new clustering index to determine the number of clusters and imposes a threshold to solve the problem caused by insufficient similar days. Our experiments showed that the proposed method reduced the mean absolute percentage error (MAPE) about 15%, compared to the PSF method.

• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.481-486
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• 2023

Anomaly detection holds paramount significance across diverse fields, encompassing fraud detection, risk mitigation, and sensor evaluation tests. Its pertinence extends notably to the military, particularly within the Warrior Platform, a comprehensive combat equipment system with wearable sensors. Hence, we propose a data-compression-based anomaly detection approach tailored to unlabeled time series and sequence data. This method entailed the construction of two distinctive features, typicality and atypicality, to discern anomalies effectively. The typicality of a test sequence was determined by evaluating the compression efficacy achieved through the pattern dictionary. This dictionary was established based on the frequency of all patterns identified in a training sequence generated for each sensor within Warrior Platform. The resulting typicality served as an anomaly score, facilitating the identification of anomalous data using a predetermined threshold. To improve the performance of the pattern dictionary method, we leveraged atypicality to discern sequences that could undergo compression independently without relying on the pattern dictionary. Consequently, our refined approach integrated both typicality and atypicality, augmenting the effectiveness of the pattern dictionary method. Our proposed method exhibited heightened capability in detecting a spectrum of unpredictable anomalies, fortifying the stability of wearable sensors prevalent in military equipment, including the Army TIGER 4.0 system.

• Mycobiology
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• v.46 no.1
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• pp.72-78
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• 2018

The fruiting body pattern is an important agronomic trait of the edible fungus Auricularia auricula-judae, and an important breeding target. There are two types of fruiting body pattern: the cluster type and the chrysanthemum type. We identified the fruiting body pattern of 26 test strains, and then constructed two different near-isogenic pools. Then, we developed sequence characterized amplified region (SCAR) molecular markers associated with the fruiting body pattern based on sequence-related amplified polymorphism (SRAP) markers. Ten different bands (189-522 bp) were amplified using 153 pairs of SRAP primers. The SCAR marker "SCL-18" consisted of a single 522-bp band amplified from the cluster-type strains, but not the chrysanthemum strains. This SCAR marker was closely associated with the cluster-type fruiting body trait of A. auricula-judae. These results lay the foundation for further research to locate and clone genes controlling the fruiting body pattern of A. auricula-judae.

• The Journal of the Korea Contents Association
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• v.8 no.9
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• pp.19-26
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• 2008

With the development of ubiquitous computing and the construction of infrastructure for one-to-one personalized services, the importance of context-aware services based on user's situation and environment is being spotlighted. The smart home technology connects real space and virtual space, and converts situations in reality into information in a virtual space, and provides user-oriented intelligent services using this information. In this paper, we proposed the discovery of the behavior sequence pattern using the mining in the smart home. We discovered the behavior sequence pattern by using mining to add time variation to the association rule between locations that occur in location transactions. We can predict the path or behavior of user according to the recognized time sequence and provide services accordingly. To evaluate the performance of behavior consequence pattern using mining, we conducted sample t-tests so as to verify usefulness. This evaluation found that the difference of satisfaction by service was statistically meaningful, and showed high satisfaction.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.196-202
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• 2021

DNA sequencing provides fundamental data in genomics, bioinformatics, biology and many other research areas. With the emergent evolution in DNA sequencing technology, a massive amount of genomic data is produced every day, mainly DNA sequences, craving for more storage and bandwidth. Unfortunately, managing, analyzing and specifically storing these large amounts of data become a major scientific challenge for bioinformatics. Those large volumes of data also require a fast transmission, effective storage, superior functionality and provision of quick access to any record. Data storage costs have a considerable proportion of total cost in the formation and analysis of DNA sequences. In particular, there is a need of highly control of disk storage capacity of DNA sequences but the standard compression techniques unsuccessful to compress these sequences. Several specialized techniques were introduced for this purpose. Therefore, to overcome all these above challenges, lossless compression techniques have become necessary. In this paper, it is described a new DNA compression mechanism of pattern matching extended Compression algorithm that read the input sequence as segments and find the matching pattern and store it in a permanent or temporary table based on number of bases. The remaining unmatched sequence is been converted into the binary form and then it is been grouped into binary bits i.e. of seven bits and gain these bits are been converted into an ASCII form. Finally, the proposed algorithm dynamically calculates the compression ratio. Thus the results show that pattern matching extended Compression algorithm outperforms cutting-edge compressors and proves its efficiency in terms of compression ratio regardless of the file size of the data.