Data Mining at IgMin Research | Technology Group

Our mission is to foster interdisciplinary dialogue and accelerate the advancement of knowledge across a wide spectrum of scientific domains.


Data mining, a pivotal field in technology and computer science, focuses on discovering patterns, correlations, and insights within vast datasets. At IgMin Research, we delve into the realm of data mining to unravel its multifaceted significance and applications. Our mission is to foster a collaborative environment where researchers and practitioners can explore the latest advancements, share groundbreaking discoveries, and collectively push the boundaries of data mining.

In this digital age, data accumulates at an unprecedented rate, rendering traditional analysis methods ineffective. Data mining, often referred to as knowledge discovery in databases (KDD), offers innovative techniques to transform raw data into actionable knowledge. Our dedicated section on data mining dives deep into its methodologies, algorithms, and real-world implementations.

  • Classification and Regression Analysis
  • Clustering and Unsupervised Learning
  • Association Rule Mining
  • Text and Web Mining
  • Spatial and Temporal Data Mining
  • Stream Data Mining
  • Graph Mining
  • Big Data Analytics
  • Predictive Analytics
  • Data Preprocessing Techniques
  • Feature Selection and Dimensionality Reduction
  • Anomaly Detection
  • Bioinformatics Applications
  • Social Network Analysis
  • Healthcare Informatics
  • Business Intelligence
  • Fraud Detection
  • Image and Video Analysis
  • Natural Language Processing
  • Sentiment Analysis
  • Market Basket Analysis
  • Recommender Systems
  • Customer Segmentation
  • Environmental Data Analysis
  • Educational Data Mining

Technology Group (1)

Review Article Article ID: igmin123

Open Access Policy refers to a set of principles and guidelines aimed at providing unrestricted access to scholarly research and literature. It promotes the free availability and unrestricted use of research outputs, enabling researchers, students, and the general public to access, read, download, and distribute scholarly articles without financial or legal barriers. In this response, I will provide you with an overview of the history and latest resolutions related to Open Access Policy.

A Survey of Motion Data Processing and Classification Techniques Based on Wearable Sensors
by Xiaoqiong Xiong, Xuemei Xiong, Chao Lian and Keda Zeng

The rapid development of wearable technology provides new opportunities for action data processing and classification techniques. Wearable sensors can monitor the physiological and motion signals of the human body in real-time, providing rich data sources for health monitoring, sports analysis, and human-computer inter...action. This paper provides a comprehensive review of motion data processing and classification techniques based on wearable sensors, mainly including feature extraction techniques, classification techniques, and future development and challenges. First, this paper introduces the research background of wearable sensors, emphasizing their important applications in health monitoring, sports analysis, and human-computer interaction. Then, it elaborates on the work content of action data processing and classification techniques, including feature extraction, model construction, and activity recognition. In feature extraction techniques, this paper focuses on the content of shallow feature extraction and deep feature extraction; in classification techniques, it mainly studies traditional machine learning models and deep learning models. Finally, this paper points out the current challenges and prospects for future research directions. Through in-depth discussions of feature extraction techniques and classification techniques for sensor time series data in wearable technology, this paper helps promote the application and development of wearable technology in health monitoring, sports analysis, and human-computer interaction.Index Terms: Activity recognition, Wearable sensor, Feature extraction, Classification

Data Mining Technology and SocietyMachine Learning