Research

In Data Mining lab, we utilise large-scale real-world data to conduct data analysis and derive significant insights. Our primary research topics include:

• Data Classification: Developing methods to classify data into predefined categories.

• Data Clustering: Researching techniques to group similar data points into clusters.

• Trajectory Data Mining: Analyzing movement data to identify patterns and extract meaningful information.

• Social Network Analysis: Examining the relationships and structures within social networks to understand their characteristics.

• Indexing: Creating efficient index structures to manage and retrieve data in databases.

• Data Stability: Ensuring the consistency and reliability of data through various methodologies.

Direction #1. Algorithmic Data Mining: We employ traditional algorithm-based methodologies to address the aforementioned topics. Our focus is on designing and developing efficient and effective algorithms to solve complex data mining problems. 

Direction #2. Data Mining + X: We employ other methodologies to perform data mining tasks. This includes applying various techniques such as deep-learning based approaches such as embeddings, attention mechanisms, large language models to enhance our data mining processes, and quantum algorithm.