• Journal of KIISE:Databases
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• v.36 no.6
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• pp.411-421
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• 2009

In a blog network, there are special users who induce other users to actively utilize blog services. In this paper, these users whose contents exhibit large influence over other bloggers are defined as 'Content Power Users' (CPUs). It is important to accurately determine who content power users are in a blog network in order to establish business policies that will stimulate usage of blog services. In this paper, we discuss a novel method of determining content power users. First, we propose a system of measuring the influence of content of each post owned by individual users. Then, by adjusting the measured values based on the time of exposure and adding them up, we calculate the power of influence for corresponding users. Finally, by applying the proposed method to actual blog networks and comparing the selected power users to those of a preexisting method, we analyze different methods of determining power users. The experimental results demonstrate that our method of determining power users reflects well dynamic changes in a blog network.

• Journal of KIISE:Databases
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• v.36 no.4
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• pp.327-340
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• 2009

To achieve the noise reduction effect in boundary image matching, we use the moving average transform of time-series matching. Our motivation is based on an intuition that using the moving average transform we may exploit the noise reduction effect in boundary image matching as in time-series matching. To confirm this simple intuition, we first propose $\kappa$-order image matching, which applies the moving average transform to boundary image matching. A boundary image can be represented as a sequence in the time-series domain, and our $\kappa$-order image matching identifies similar images in this time-series domain by comparing the $\kappa$-moving average transformed sequences. Next, we propose an index-based matching method that efficiently performs $\kappa$-order image matching on a large volume of image databases, and formally prove the correctness of the index-based method. Moreover, we formally analyze the relationship between an order $\kappa$ and its matching result, and present a systematic way of controlling the noise reduction effect by changing the order $\kappa$. Experimental results show that our $\kappa$-order image matching exploits the noise reduction effect, and our index-based matching method outperforms the sequential scan by one or two orders of magnitude.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.217-245
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• 2016

The purpose of this study is to verify how 7 sentiment domains extracted through sentiment analysis from social media have an influence on business performance. It consists of three phases. In phase I, we constructed the sentiment lexicon after crawling 45,447 pieces of VOC (Voice of the Customer) on 26 auto companies from the car community and extracting the POS information and built a seven-sensitive domains. In phase II, in order to retain the reliability of experimental data, we examined auto-correlation analysis and PCA. In phase III, we investigated how 7 domains impact on the market share of three major (GM, FCA, and VOLKSWAGEN) auto companies by using linear regression analysis. The findings from the auto-correlation analysis proved auto-correlation and the sequence of the sentiments, and the results from PCA reported the 7 sentiments connected with positivity, negativity and neutrality. As a result of linear regression analysis on model 1, we indentified that the sentimental factors have a significant influence on the actual market share. In particular, not only posotive and negative sentiment domains, but neutral sentiment had significantly impacted on auto market share. As we apply the availability of data to the market, and take advantage of auto-correlation of the market-related information and the sentiment, the findings will be a huge contribution to other researches on sentiment analysis as well as actual business performances in various ways.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.279-300
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• 2001

Data mining plays an important role in knowledge discovery process and usually various existing algorithms are selected for the specific purpose of the mining. Currently, data mining techniques are actively to the statistics, business, electronic commerce, biology, and medical area and currently numerous algorithms are being researched and developed for these applications. However, in a long run, only a few algorithms, which are well-suited to specific applications with excellent performance in large database, will survive. So it is reasonable to focus our effort on those selected algorithms in the future. This paper classifies about 30 existing algorithms into 7 categories - association rule, clustering, neural network, decision tree, genetic algorithm, memory-based reasoning, and bayesian network. First of all, this work analyzes systematic hierarchy and characteristics of algorithms and we present 14 criteria for classifying the algorithms and the results based on this criteria. Finally, we propose the best algorithms among some comparable algorithms with different features and performances. The result of this paper can be used as a guideline for data mining researches as well as field applications of data mining.

• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.80-90
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• 2002

Clustering is a useful technique for grouping data points such that points within a single group/cluster have similar characteristics. Many clustering algorithms have been developed and used in engineering applications including pattern recognition and image processing etc. Recently, it has drawn increasing attention as one of important techniques in data mining. However, clustering algorithms such as K-means and Fuzzy C-means suffer from difficulties. Those are the needs to determine the number of clusters apriori and the clustering results depending on the initial set of clusters which fails to gain desirable results. In this paper, we propose a new clustering algorithm, which solves mentioned problems. In our method we use evolutionary algorithm to solve the local optima problem that clustering converges to an undesirable state starting with an inappropriate set of clusters. We also adopt a new measure that represents how well data are clustered. The measure is determined in terms of both intra-cluster dispersion and inter-cluster separability. Using the measure, in our method the number of clusters is automatically determined as the result of optimization process. And also, we combine heuristic that is problem-specific knowledge with a evolutionary algorithm to speed evolutionary algorithm search. We have experimented our algorithm with several sets of multi-dimensional data and it has been shown that one algorithm outperforms the existing algorithms.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.19-43
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• 2016

This study examines the causes of the Asian exchange rate crisis and compares it to the European Monetary System crisis. In 1997, emerging countries in Asia experienced financial crises. Previously in 1992, currencies in the European Monetary System had undergone the same experience. This was followed by Mexico in 1994. The objective of this paper lies in the generation of useful insights from these crises. This research presents a comparison of South Korea, United Kingdom and Mexico, and then compares three different models for prediction. Previous studies of economic crisis focused largely on the manual construction of causal models using linear techniques. However, the weakness of such models stems from the prevalence of nonlinear factors in reality. This paper uses a structural equation model to analyze the causes, followed by a neural network model to circumvent the linear model's weaknesses. The models are examined in the context of predicting exchange rates In this paper, data were quarterly ones, and Consumer Price Index, Gross Domestic Product, Interest Rate, Stock Index, Current Account, Foreign Reserves were independent variables for the prediction. However, time periods of each country's data are different. Lisrel is an emerging method and as such requires a fresh approach to financial crisis prediction model design, along with the flexibility to accommodate unexpected change. This paper indicates the neural network model has the greater prediction performance in Korea, Mexico, and United Kingdom. However, in Korea, the multiple regression shows the better performance. In Mexico, the multiple regression is almost indifferent to the Lisrel. Although Lisrel doesn't show the significant performance, the refined model is expected to show the better result. The structural model in this paper should contain the psychological factor and other invisible areas in the future work. The reason of the low hit ratio is that the alternative model in this paper uses only the financial market data. Thus, we cannot consider the other important part. Korea's hit ratio is lower than that of United Kingdom. So, there must be the other construct that affects the financial market. So does Mexico. However, the United Kingdom's financial market is more influenced and explained by the financial factors than Korea and Mexico.