Yonsei University · 情報科学
Professor Wooju Kim's research lab specializes in data-driven intelligent systems with a focus on knowledge discovery, pattern recognition, and predictive analytics. The lab explores advanced techniques in text mining, demand forecasting, fraud detection, and knowledge graph analysis, particularly leveraging machine learning, sequence pattern mining, and subgraph mining for real-world applications. Research directions include improving text classification through cosine similarity, enhancing indoor localization via signal fusion, and developing personalized models for financial fraud detection and e-commerce negotiation systems. The lab emphasizes practical, scalable solutions using big data from wireless sensors, social media, and transaction logs.
Figures are computed from collected data and may differ slightly.
Text Classification has received significant attention in recent years because of the proliferation of digital documents and is widely used in various applications such as filtering and recommendation. Consequently, many approaches, including those based on statistical theory, machine learning, and classifier performance improvement, have been proposed for improving text classification performance. Among these approaches, centroid-based classifier, multinomial naïve bayesian (MNB), support vecto
Ubiquitous indoor environments often contain substantial amounts of metal and other similar reflective materials that affect the propagation of radio frequency signals in important ways, causing severe multipath effects, including noise and interference, when measuring the signal strength between sender and receiver. To minimize the noise level, this study proposes advanced fusion filter (AFF) and improved fusion filter (IFF) using received signal strength indicator (RSSI) and link quality indic
Demand forecasting in the biomedical area is becoming more important because of radical changes in the macroeconomic environment and consumption trends. Moreover, the need for big data analysis on data from wireless sensor networks and social media is increasing because it shows not only the rapidly changing environmental data such as fine dust concentration but also the responses of potential customers that are expected to affect the demand for a medicine. Therefore, demand forecasting models b
Financial institutions face challenges of fraud due to an increased number of online transactions and sophisticated fraud techniques. Although fraud detection systems have been implemented to detect fraudulent transactions in online banking, many systems just use conventional rule-based approaches. Rule-based detection systems have a difficulty in updating and managing their rules and conditions manually. Additionally, generated from the few fraud cases, the rules are general rather than specifi
As more and more consumer-to-consumer e-marketplaces have been introduced with rapidly increasing transaction volumes, consumers now hope that computer technology will support their transaction processes in these electronic markets. To satisfy their expectations, many researchers have delved into developing intelligent agent systems to support customer-to-customer electronic commerce more efficiently. However, many of these researchers have focused only upon supporting simple negotiation for the
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