• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• Journal of KIISE:Software and Applications
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• v.29 no.6
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• pp.367-376
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• 2002

XML allows users to define elements using arbitrary words and organize them in a nested structure. These features of XML offer both challenges and opportunities in information retrieval and document management. In this paper, we propose a new methodology for computing similarity considering XML semantics - meanings of the elements and nested structures of XML documents. We generate extended-element vectors, using thesaurus, to normalize synonyms, compound words, and abbreviations and build similarity matrix using them. And then we compute similarity between XML elements. We also discover and minimize XML structure using automata(NFA(Nondeterministic Finite Automata) and DFA(Deterministic Finite automata). We compute similarity between XML structures using similarity matrix between elements and minimized XML structures. Our methodology considering XML semantics shows 100% accuracy in identifying the category of real documents from on-line bookstore.

• Journal of the Korea Society of Computer and Information
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• v.10 no.6 s.38
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• pp.93-102
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• 2005

This paper proposes an approach to verify signature using autonomous self-organized Neural Network Model , fused with fuzzy membership equation of fuzzy c-means algorithm, based on the features of the signature. To overcome limitations of the functional approach and Parametric approach among the conventional on-line signature recognition approaches, this Paper presents novel autonomous signature classification approach based on clustering features. Thirty-six globa1 features and twelve local features were defined, so that a signature verifying system with FE-SONN that learns them was implemented. It was experimented for total 713 signatures that are composed of 155 original signatures and 180 forged signatures yet 378 original signatures written by oneself. The success rate of this test is more than 97.67$\%$ But, a few forged signatures that could not be detected by human eyes could not be done by the system either.

• Journal of Physiology & Pathology in Korean Medicine
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• v.18 no.4
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• pp.1028-1035
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• 2004

Imperatorin, a biologically active furanocoumarin from the roots of Angelica dahurica (Umbelliferae), was mutagenic and induced transformation of mouse fibroblast cell lines, whereas it provided inhibiting effects on mutagenesis and carcinogenesis induced by various carcinogens. Furthermore, it has been suggested that imperatorin may have potential anticarcinogenic effects when administered orally in the diet. In addition to its anticarcinogenic properties, imperatorin has been shown to possess anticancer activities. We investigated the macro scale gene expression analysis on the HL-60 cells treated with imperatorin. Imperatorin (10μM) were used to treat the cells for 6h, 12h, 24h, 48h, and 72h. In a human cDNAchip study of 10,000 genes evaluated 6, 12, 24, 48, 72 hours after treated with imperatorin in HL-60 cells. Hierarchical cluster against the genes which showed expression changes by more than 2 fold. Three hundred eighty six genes were grouped into 6 clusters by a hierarchical clustering algorithm. Pathway analysis using gene microarray pathway prof Her that is a computer application designed to visualize gene expression data on screen representing biological pathways and groupings of genes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.5
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• pp.2383-2389
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• 2011

Ubiquitous applications require hybrid continuous query processing which processes both on-line data stream and spatial data in the disk. In the hybrid continuous spatial query processing, disk access costs for the high-volume spatial data should be minimized. However, previous indexing methods cannot reduce the disk seek time, because it is difficult that the data are stored in contiguity with others. Also, existing methods for the space-filling curve considering data cluster have the problem which does not cluster available data for queries. Therefore, we propose the region storage structure for efficient data access in hybrid continues spatial query processing. This paper shows that there is an obvious improvement of query processing costs through the contiguous data storing method and the group processing for user queries based on the region storage structure.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.169-189
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• 2005

In order to derive a better classifier with a limited number of training examples, active teaming alternately repeats the querying stage fur category labeling and the subsequent learning stage fur rebuilding the calssifier with the newly expanded training set. To relieve the user from the burden of labeling, especially in an on-line environment, it is important to minimize the number of querying steps as well as the total number of query examples. We can derive a good classifier in a small number of querying steps by using only a small number of examples if we can select multiple of diverse, representative, and ambiguous examples to present to the user at each querying step. In this paper, we propose a cluster-based batch query selection method which can select diverse, representative, and highly ambiguous examples for efficient active learning. Experiments with various text data sets have shown that our method can derive a better classifier than other methods which only take into account the ambiguity as the criterion to select multiple query examples.

• Journal of KIISE:Databases
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• v.30 no.3
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• pp.225-236
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• 2003

MOLAP systems store data in a multidimensional away called a 'cube' and access them using way indexes. When a cube is placed into disk, it can be Partitioned into a set of chunks of the same side length. Such a cube storage scheme is called the chunk-based MOLAP cube storage scheme. It gives data clustering effect so that all the dimensions are guaranteed to get a fair chance in terms of the query processing speed. In order to achieve high space utilization, sparse chunks are further compressed. Due to data compression, the relative position of chunks cannot be obtained in constant time without using indexes. In this paper, we propose a bitmap index for chunk-based MOLAP cubes. The index can be constructed along with the corresponding cube generation. The relative position of chunks is retained in the index so that chunk retrieval can be done in constant time. We placed in an index block as many chunks as possible so that the number of index searches is minimized for OLAP operations such as range queries. We showed the proposed index is efficient by comparing it with multidimensional indexes such as UB-tree and grid file in terms of time and space.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11a
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• pp.201-204
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• 2003

At a consequence of WWW large popularity, the internet has suffered from various performance problems, such as network congestion and overloaded servers. These days, it is not uncommon to find servers refusing connections because they are overloaded. Web server performance has always been a key issue in the design and operation of on-line systems. With regard to Internet, performance is also critical, because users want fast and easy access to all objects (e.g., documents, graphics, audio, and video) available on the net. To solve this problem, a number of companies are exploring the benefits of having multiple geographically or locally distributed Internet sites. This requires a comprehensive scheme for traffic management, which includes the principle of an optimal load balancing of client requests across multiple clusters of real servers. This paper focuses on the performance analysis of Web server and we apply these results to load balancing in clustering web server. It also discusses the mam steps needed to carry out a WWW performance analysis effort and shows relations between the workload characteristics and system resource usage. Also, we will introduce an optimal load balancing algorithm base on the RTSM (Real-Time Server Monitor) and Fuzzy Inference Engine for the local status of a real server, and the benefits is provided with of the suggested method.

• Molecular & Cellular Toxicology
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• v.2 no.2
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• pp.120-125
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• 2006

Phenytoin is an anti-epileptic. It works by slowing down impulses in the brain that cause seizures. The recent microarray technology enables us to understand possible mechanisms of genes related to compounds which have toxicity in biological system. We have studied that the effect of a compound related to hepatotoxin in vitro system using a rat whole genome microarray. In this study, we have used a rat liver epithelial cell line WB-F344 and phenytoin as a hepatotoxin. WB-F344 was treated with phenytoin for 1 to 24 hours. Total RNA was isolated at times 1, 6 and 24h following treatment of phenytoin, and hybridized to the microarray containing about 22,000 rat genes. After analysis with clustering methods, we have identified a total of 1,455 differentially expressed genes during the time course. Interestingly, about 1,049 genes exhibited differential expression pattern in response to phenytoin in early time. Therefore, the identification of genes associated with phenytoin in early response may give important insights into various toxicogenomic studies in vitro system.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.8
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• pp.4533-4537
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• 2013

Objective: To use microarray chip technology for screening of stem cell radiation related miRNAs in laryngeal squamous cell carcinoma; study and explore the relationship of miRNAs with radiosensitivity of laryngeal squamous cells. Method: After conventional culture and amplification of the laryngeal squamous carcinoma cell line Hep-2, CD 133+ cells were screened out with combination of isolated culture of stem cell microspheres and FACS for preparation of laryngeal cancer stem cells. After radiation treatment, miRNAs of laryngeal squamous carcinoma stem cells before and after radiation were enriched and purified. After microarray hybridization with mammalian miRNA and scanning of fluorescence signal, the miRNAs of laryngeal squamous carcinoma stem cells before and after radiation was subject to differential screening and clustering analysis. Real-time quantitative RT-PCR was used to verify part of the differentially expressed miRNAs. Results: 70 miRNAs related to laryngeal cancer stem cell radiation with 2-fold difference in expression were screened out, in which 62 were down-regulated and 8 were up-regulated. Fluorescent quantitative RT-PCR results were consistent with miRNAs chip results. Conclusion: Some miRNAs may be involved in self-regulation with laryngeal squamous carcinoma stem cell radiation.