10^th International Conference on Data Mining (DaMi 2024)

June 22 ~ 23, 2024, Sydney, Australia

https://csit2024.org/dami/index

Scope

10^th International Conference on Data Mining (DaMi 2024) Conference provides a forum for researchers who address this issue and to present their work in a peer-reviewed forum.

Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Data Mining and Knowledge Management Process.

Topics of interest include, but are not limited to, the following

·         Data Mining Foundations

·         Parallel and Distributed Data mining Algorithms

·         Data Streams Mining, Graph Mining

·         Spatial Data mining, Text Video

·         Multimedia Data Mining

·         Web Mining

·         Pre-processing Techniques

·         Visualization

·         Security and Information Hiding in Data Mining

·         Data Mining Applications

·         Databases

·         Bioinformatics

·         Biometrics

·         Imge Analysis

·         Financial Modeling

·         Forecasting

·         Classification

·         Clustering

·         Social Networks

·         Educational Data Mining

·         Knowledge Processing

·         Data and Knowledge Representation

·         Knowledge Discovery Framework and Process

·         Including Pre- and Post-Processing

·         Integration of Data Warehousing

·         OLAP and Data Mining

·         Integrating Constraints and Knowledge in the KDD Process

·         Exploring Data Analysis

·         Inference of Causes

·         Prediction

·         Evaluating

·         Consolidating

·         Explaining Discovered Knowledge

·         Statistical Techniques for Generating a Robust

·         Consistent Data Model

·         Interactive Data Exploration/Visualization and Discovery

·         Languages and Interfaces for Data Mining

·         Mining Trends

·         Opportunities and Risks

·         Mining From Low-Quality Information Sources