• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.7
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• pp.768-774
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• 2016

This paper deals with a method to recommend the combination of items as a group according to similarity to handle application area such as fashion and cooking, while the previous methods recommend single item such as a book, music or movie. Collaborative filtering is a method to recommend an item selected by users with similar tendency based on similarity between users. In this paper, the proposed method generates a set of frequent items based on collaborative filtering and association rules and recommends a group by similarity between groups. To show the validity of the proposed method, experiments are performed with purchase data collected from e-commerce for four months.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.3
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• pp.203-210
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• 2000

In this paper, we propose a new unsupervised learning network and competitive learning algorithm for pattern classification. The proposed network is based on relative similarity, which is similarity measure between input data and cluster group. So, the proposed network and algorithm is called relative similarity network(RSN) and learning algorithm. According to definition of similarity and learning rule, structure of RSN is designed and pseudo code of the algorithm is described. In general pattern classification, RSN, in spite of deletion of learning rate, resulted in the identical performance with those of WTA, and SOM. While, in the patterns with cluster groups of unclear boundary, or patterns with different density and various size of cluster groups, RSN produced more effective classification than those of other networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4176-4197
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• 2020

Botnet is a type of dangerous malware. Botnet attack with a collection of bots attacking a similar target and activity pattern is called bot group activities. The detection of bot group activities using intrusion detection models can only detect single bot activities but cannot detect bots' behavioral relation on bot group attack. Detection of bot group activities could help network administrators isolate an activity or access a bot group attacks and determine the relations between bots that can measure the correlation. This paper proposed a new model to measure the similarity between bot activities using the intersections-probability concept to define bot group activities called as B-Corr Model. The B-Corr model consisted of several stages, such as extraction feature from bot activity flows, measurement of intersections between bots, and similarity value production. B-Corr model categorizes similar bots with a similar target to specify bot group activities. To achieve a more comprehensive view, the B-Corr model visualizes the similarity values between bots in the form of a similar bot graph. Furthermore, extensive experiments have been conducted using real botnet datasets with high detection accuracy in various scenarios.

• Journal of the Korea Society of Computer and Information
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• v.23 no.10
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• pp.87-94
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• 2018

This paper proposes a method of detecting the license plates of vehicles. The proposed technology applicable to different formats of license plates detects the numbers by standardizing the images at edge points. Specifically, in accordance with the format of each license plate, the technology captures the image in the character segment, and compares it against the sample model to derive their similarity and identify the numbers. Characters with high similarities are used to form a group of candidates and to extract the final characters. Analyzing the experimental results found the similarity of the extracted characters exceeded 90%, whereas that of less identifiable numbers was markedly lower. Still, the accuracy of the extracted characters with the highest similarity was over 80%. The proposed technology is applicable to extracting the character patterns of certain formats in diverse and useful ways.

• Journal of Ginseng Research
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• v.32 no.1
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• pp.15-18
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• 2008

This study was carried out to evaluate the basic information on ginsenoside contents and pattern similarity in five cultivars of Panax ginseng C.A. Meyer. Among five cultivars the unit content and total content of ginsenosides were the highest in Gopoong cultivar as 18.9 mg/g and 596 mg/root, respectively. The unit content and total content of ginsenosides decreased in the order of Yunpoong, Gumpoong, Seonpoong and Chunpoong cultivar. Ginsenoside pattern similarity between tap root and lateral root was high as 0.95 but that between tap root and fine root was low as 0.72. Correlation of ginsenoside contents between tap root and lateral root exhibited the highest value as 0.843 and decreased in the order of main root, fine root, and rhizome. And the correlation value between unit content and total content of ginsenoside was very high as 0.933.

• Journal of Korea Multimedia Society
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• v.12 no.1
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• pp.107-121
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• 2009

First reason for generation of standby power is because starting voltage must pass through from the source of electricity to IC. The second reason is due to current when IC is in operation. Purpose of this abstract is on structures of simple modules that automatically switch on or off through analysis of state on standby power and analysis of cutoff point patterns as well as application of algorithms. To achieve this, this paper is based on analysis of electric signals and modeling. Also, on/off cutoff criteria has been established for reduction of standby power. To find on/off cutoff point, that is executed algorithm of similar group and leading pattern group generation in the standby power state. Therefore, the algorithm was defined as an important parameter of the subtraction value of calculated between $1^{st}$ SCS, $2^{nd}$ SCS, and the median value of sampling coefficient per second from a wall outlet.

• The Korea Journal of Herbology
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• v.28 no.6
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• pp.9-14
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• 2013

Objectives : Cnidii Fructus is prescribed as the fruit of Cnidium monnieri (L.) Cusson or Torilis japonica (Houtt.) DC. in Korea pharmacopoeia. Although there are differences in the composition of useful components, two species have been used without distinction. In order to discriminate them, DNA sequencing and taste pattern analysis were used in this study. Methods : Primers ITS 1 and ITS 4 were used to amplify the intergenic transcribed spacer(ITS) region of nuclear ribosomal DNA from seven T. japonica and six C. monnieri samples. Taste pattern of samples were measured by using taste-sensing system SA402B equipped with five foodstuff sensors(CT0, C00, AAE, CA0, and AE1). The five initial taste(sourness, bitterness, astringency, umami, and saltiness) and three aftertaste(aftertaste of bitterness, astringency, and umami) of two species were compared. Results : According to the results of ITS region sequence analysis, two species showed 94 base pairs differences. The similarity of two sequences was 85%. From the taste pattern analysis, sourness, bitterness, aftertaste of bitterness(aftertaste-B), and umami showed a different pattern. Especially, bitterness and aftertaste-B of C. monnieri were significantly higher than T. japonica. In addition, two species were shown to have two markedly different clustering by these two flavors. Conclusion : T. japonica and C. monnieri were effectively discriminated using DNA sequencing and taste pattern analysis. These methods can be used to identify the origin of traditional medicine in order to maintain therapeutic efficacy.

• Journal of Intelligence and Information Systems
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• v.14 no.1
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• pp.1-17
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• 2008

Data mining is defined as the process of discovering meaningful and useful pattern in large volumes of data. In particular, finding associations rules between items in a database of customer transactions has become an important thing. Some data structures and algorithms had been proposed for storing meaningful information compressed from an original database to find frequent itemsets since Apriori algorithm. Though existing method find all association rules, we must have a lot of process to analyze association rules because there are too many rules. In this paper, we propose a new data structure, called a Frequent Pattern Network (FPN), which represents items as vertices and 2-itemsets as edges of the network. In order to utilize FPN, We constitute FPN using item's frequency. And then we use a clustering method to group the vertices on the network into clusters so that the intracluster similarity is maximized and the intercluster similarity is minimized. We generate association rules based on clusters. Our experiments showed accuracy of clustering items on the network using confidence, correlation and edge weight similarity methods. And We generated association rules using clusters and compare traditional and our method. From the results, the confidence similarity had a strong influence than others on the frequent pattern network. And FPN had a flexibility to minimum support value.

• Journal of Environmental Impact Assessment
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• v.18 no.6
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• pp.401-409
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• 2009

Multivariate statistical approaches to classify sampling sites with measuring their similarity by water quality data and understand the characteristics of classified clusters have been discussed for the optimal water quality monitering network. For empirical study, data of two years (2005, 2006) at the 9 sampling sites with the combination of 2 depth levels and 7 important variables related to water quality is collected in Yongdam reservoir. The similarity among sampling sites is measured with Euclidean distances of water quality related variables and they are classified by hierarchical clustering method. The clustered sites are discussed with principal component variables in the view of the geographical characteristics of them and reducing the number of measuring sites. Nine sampling sites are clustered as follows; One cluster of 5, 6, and 7 sampling sites shows the characteristic of low water depth and main stream of water. The sites of 2 and 4 are clustered into the same group by characteristics of hydraulics which come from that of main stream. But their changing pattern of water quality looks like different since the site of 2 is near to dam. The sampling sites of 3, 8, and 9 are individually positioned due to the different tributary.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.957-961
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• 2010

Multivariate statistical approaches to classify sampling sites with measuring their similarity by water quality data. For empirical study, data of two years at the 9 sampling sites with the combination of 2 depth levels and 7 important variables related to water quality is collected in reservoir. The similarity among sampling sites is measured with Euclidean distances of water quality related variables and they are classified by hierarchical clustering method. The clustered sites are discussed with principal component variables in the view of the geographical characteristics of them and reducing the number of measuring sites. Nine sampling sites are clustered as follows; One cluster of 5, 6, and 7 sampling sites shows the characteristic of low water depth and main stream of water. The sites of 2 and 4 are clustered into the same group by characteristics of hydraulics which come from that of main stream. But their changing pattern of water quality looks like different since the site of 2 is near to dam. The sampling sites of 3, 8, and 9 are individually positioned due to the different tributary.