• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.05a
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• pp.274-282
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• 2005

Customer retention is one of the major issued in life insurance industry, in which competition is increasingly fierce. There are many things to do to retain customers. One of those things is to be continuously in touch with all customers. The objective of this study is to design the contact scheduling system(CSS) to support the planers who must touch the customers without having subjective information. Support-planers suffer from lack of information which can be used to intimately touch. CSS that is developed in this study generates contact schedule to touch customers by taking into account existing contact history. CSS has a two stage process. In the first stage, it segments customers according to his or her demographics and contract status data. Then it finds typical pattern and pattern is combined to business rules for each segment. We expert that CSS would support support-planers to make uncontacted customers' experience positive.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.733-735
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• 2005

최근 음성인식 분야 에서는 잡음 환경에서 좀 더 신뢰도 높은 음성 인식 결과물 얻기 위하여 인식 결과 도출 단계에서 여러 가지 정보를 융합 하는 방법이나 인식결과를 후처리 하여 새로운 결과를 얻어 내는 방법들이 연구 되고 있다. 본 논문에서는 개인 모바일 기기에서의 음성 인식 환경에서 사용자의 발화 패턴 정보를 가지는 문맥 정보를 활용함으로서 잡음 환경에서의 음성 정보 손실에 따른 인식률 하락을 보완하는 방법을 제안한다. 먼저 사용자의 기기 사용 로그나 발화 로그 정보로부터 특정 명령어들의 순차적 발화 패턴을 마이닝하여 문맥 정보를 구성한다. 이 후 음성 발화시에 인식기의 최종 인식 결과에 대한 신뢰도가 떨어진다고 판단될 때 앞서 얻어진 문맥 정보의 신뢰도를 인식기의 각 후보단어들의 인식률과 융합하여 새로운 인식 결과를 도출해 낸다. 이러한 과정에서 인식기 결과에 대한 신뢰성을 판단하는 기준을 실험을 통하여 결정 하였으며 신뢰성이 기준 이하일 경우의 융합 과정을 위하여 후보 단어 인식률과 문맥정보를 적절히 융합할 수 있는 방법을 제안한다.

• Journal of Korea Spatial Information System Society
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• v.11 no.1
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• pp.127-136
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• 2009

This paper proposes an on-line mining algorithm of moving trajectory patterns in RFID data streams considering changing characteristics over time and constraints of single-pass data scan. Since RFID, sensor, and mobile network technology have been rapidly developed, many researchers have been recently focused on the study of real-time data gathering from real-world and mining the useful patterns from them. Previous researches for sequential patterns or moving trajectory patterns based on stream data have an extremely time-consum ing problem because of multi-pass database scan and tree traversal, and they also did not consider the time-changing characteristics of stream data. The proposed method preserves the sequential strength of 2-lengths frequent patterns in binary relationship table using the time-evolving graph to exactly reflect changes of RFID data stream from time to time. In addition, in order to solve the problem of the repetitive data scans, the proposed algorithm infers candidate k-lengths moving trajectory patterns beforehand at a time point t, and then extracts the patterns after screening the candidate patterns by only one-pass at a time point t+1. Through the experiment, the proposed method shows the superior performance in respect of time and space complexity than the Apriori-like method according as the reduction ratio of candidate sets is about 7 percent.

• 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 Digital Convergence
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• v.14 no.11
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• pp.267-273
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• 2016

Creation and Sharing of information which is structured data as well as various unstructured data. makes progress actively through the spread of mobile. Recently, Big Data extracts the semantic information from SNS and data mining is one of the big data technique. Especially, the general emotion analysis that expresses the collective intelligence of the masses is utilized using large and a variety of materials. In this paper, we propose the emotion prediction system architecture which extracts the significant keywords from social network paragraphs using n-gram and Korean morphological analyzer, and predicts the emotion using SVM and these extracted emotion features. The proposed system showed 82.25% more improved recall rate in average than previous systems and it will help extract the semantic keyword using morphological analysis.

• 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 the Korea Convergence Society
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• v.8 no.8
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• pp.71-75
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• 2017

Due to the spread of SNS, web-based consumer-generated data is increasing exponentially. It is important in many fields to accurately extract what is appropriate for the user's interest in a large amount of data. It is especially important for business mangers to establish marketing policies to find the right customers for them in many users. In this paper, we try to obtain important information centering on customers who are interested in each account through Twitter follow - following relationship. Because Twitter's current follower relationships do not reflect the user's interests, we try to figure out the details of interest using keyword extraction methods for tweets of followers. To do this, we select two domestic commercial Twitter accounts and apply the sequential pattern evaluation index to the mining key phrase of the text data collected from the follower.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.77-87
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• 2021

With the development of the Internet and personal computers, various and complex attacks begin to emerge. As the attacks become more complex, signature-based detection become difficult. It leads to the research on behavior-based log anomaly detection. Recent work utilizes deep learning to learn the order and it shows good performance. Despite its good performance, it does not provide any explanation for prediction. The lack of explanation can occur difficulty of finding contamination of data or the vulnerability of the model itself. As a result, the users lose their reliability of the model. To address this problem, this work proposes an explainable log anomaly detection system. In this study, log parsing is the first to proceed. Afterward, sequential rules are extracted by Bayesian posterior probability. As a result, the "If condition then results, post-probability" type rule set is extracted. If the sample is matched to the ruleset, it is normal, otherwise, it is an anomaly. We utilize HDFS datasets for the experiment, resulting in F1score 92.7% in test dataset.

• Journal of Internet Computing and Services
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• v.12 no.2
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• pp.9-17
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• 2011

This paper proposes an algorithm that predicts the state of user's next actions, exploiting the HMM (Hidden Markov Model) on user profile data stored in the ubiquitous home network. The HMM, recognizes patterns of sequential data, adequately represents the temporal property implicated in the data, and is a typical model that can infer information from the sequential data. The proposed algorithm uses the number of the user's action performed, the location and duration of the actions saved by "Activity Recognition System" as training data. An objective formulation for the user's interest in his action is proposed by giving weight on his action, and change on the state of his next action is predicted by obtaining the change on the weight according to the flow of time using the HMM. The proposed algorithm, helps constructing realistic ubiquitous home networks.

• Journal of KIISE:Databases
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• v.31 no.5
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• pp.468-478
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• 2004

It is essential in various application areas of data mining and bioinformatics to effectively retrieve the occurrences of interesting patterns from sequence databases. For example, let's consider a network event management system that records the types and timestamp values of events occurred in a specific network component(ex. router). The typical query to find out the temporal casual relationships among the network events is as fellows: 'Find all occurrences of CiscoDCDLinkUp that are fellowed by MLMStatusUP that are subsequently followed by TCPConnectionClose, under the constraint that the interval between the first two events is not larger than 20 seconds, and the interval between the first and third events is not larger than 40 secondsTCPConnectionClose. This paper proposes an indexing method that enables to efficiently answer such a query. Unlike the previous methods that rely on inefficient sequential scan methods or data structures not easily supported by DBMSs, the proposed method uses a multi-dimensional spatial index, which is proven to be efficient both in storage and search, to find the answers quickly without false dismissals. Given a sliding window W, the input to a multi-dimensional spatial index is a n-dimensional vector whose i-th element is the interval between the first event of W and the first occurrence of the event type Ei in W. Here, n is the number of event types that can be occurred in the system of interest. The problem of‘dimensionality curse’may happen when n is large. Therefore, we use the dimension selection or event type grouping to avoid this problem. The experimental results reveal that our proposed technique can be a few orders of magnitude faster than the sequential scan and ISO-Depth index methods.hods.