Welcome to Data Analytics & Information Lab

데이터 분석과 정보 연구실에 방문하신 것을 환영합니다. 본 연구실은 적극적이고 열정이 있는 대학원생(석/박사)을 모집하고 있습니다. 데이터 분석에 관심 있는 학생들은 언제든지 문의(jtseong@jnu.ac.kr) 가능합니다. 

Welcome to the Data Analytics and Information Lab. We are looking for active and enthusiastic researchers (including MS or PhD students) to join our lab. Students interested in data analysis are welcome to contact us (jtseong@jnu.ac.kr).


Latest News

Anomaly Detection

In data analysis, anomaly detection (also referred to as outlier detection and sometimes as novelty detection) is generally understood to be the identification of rare items, events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal behaviour. Such examples may arouse suspicions of being generated by a different mechanism, or appear inconsistent with the remainder of that set of data. 

Detection of Electricity Theft based Learning Technieques

Electricity theft refers to the unauthorized and illegal consumption of electricity without proper billing or payment. This illicit activity poses significant challenges for power utilities, leading to revenue losses and increased operational costs. It involves various techniques, such as tampering with meters, illegal connections, and meter bypassing, which not only undermines the financial stability of utility companies but also disrupts the overall power distribution system. Combating electricity theft requires implementing robust monitoring systems, raising public awareness, and enforcing stringent penalties to deter such practices 

Label Noise  in Supervised Learning
Label noise in supervised learning refers to the presence of incorrect or inaccurate labels in the training data. In this paradigm, the algorithm learns from data where each input is associated with a corresponding target label. However, real-world datasets can be prone to errors, mislabeling, or ambiguity, which introduces label noise. Addressing label noise is crucial as it can severely impact the model's performance and generalization ability, necessitating the use of various data cleaning, regularization, and ensemble techniques to mitigate its adverse effects and improve the reliability of the learned model. 

Group Testing

Group testing is a technique where multiple samples are combined into pools and tested together to identify positive cases efficiently. By testing multiple samples at once, group testing reduces the number of tests required and saves resources. Group testing is used in various fields such as healthcare, public health, and agriculture for screening, surveillance, and control of diseases. Group testing can detect positive cases with high sensitivity and specificity while reducing the burden on the healthcare system. Group testing requires a certain level of prevalence, and additional confirmatory tests may be needed for positive pools. COVID-19 testing of asymptomatic individuals in a community can be done using group testing to identify positive cases efficiently.

 

Latent Factor Analysis 

Latent factor analysis is a statistical technique used to identify the underlying structure of a set of variables. It assumes that there are underlying factors that influence the observed variables and seeks to identify those factors. It is commonly used in fields such as psychology, education, marketing, and finance. The technique involves several steps, including data collection, data preprocessing, factor extraction, and factor rotation. The results of the analysis can provide insights into the relationships between the observed variables and the underlying factors. It can also be used for predictive modeling and dimensionality reduction.

 

Compressive Sensing

Compressive sensing is a signal processing technique used to efficiently acquire and process high-dimensional data with fewer measurements than traditional methods. It assumes that the signal to be acquired is sparse, meaning it has few non-zero coefficients in a known or unknown basis. It allows for data to be acquired at a lower rate and then reconstructed with high accuracy, reducing data acquisition and storage costs. The technique involves several steps, including signal acquisition, signal processing, and sparse signal recovery. Compressive sensing has applications in a wide range of fields, including image and video processing, wireless communication, medical imaging, and more. The technique is still an active area of research, with ongoing efforts to improve its efficiency and accuracy.

Contact Information