Wha is data mining
Pro Research Analysisby 
Searched over 200M research papers
Understanding Data Mining: Concepts, Techniques, and Applications
Introduction to Data Mining
Data mining is the process of discovering interesting, unexpected, or valuable patterns and structures in large datasets. It is an interdisciplinary field that combines techniques from statistics, machine learning, database technology, and other areas to analyze and interpret complex data1 3 5. The primary goal of data mining is to extract useful information from large volumes of data and transform it into an understandable structure for future use9.
Key Aspects of Data Mining
Global and Local Structures
Data mining involves two main aspects: identifying large-scale, global structures and detecting small-scale, local anomalies. Global structures aim to model the shapes or features of distributions, while local structures focus on identifying anomalies and determining if they are significant or random occurrences1.
Patterns and Knowledge Discovery
The process of data mining includes discovering patterns, associations, changes, anomalies, and significant structures from large datasets stored in databases, data warehouses, or other information repositories. This process is often referred to as knowledge discovery in databases (KDD)4. The KDD process typically involves several iterative steps: data integration, data selection, data transformation, data mining, pattern evaluation, and knowledge presentation4.
Data Mining Techniques
Predictive and Descriptive Data Mining
Data mining techniques can be broadly categorized into predictive and descriptive methods. Predictive data mining involves using variables in the dataset to predict unknown or future values of other variables. Descriptive data mining focuses on finding patterns that describe the data and can be interpreted by humans8.
Common Techniques
Several techniques are commonly used in data mining, including:
- Classification: Assigning items to predefined categories or classes.
- Clustering: Grouping similar items together based on their characteristics.
- Association Rule Learning: Discovering interesting relationships between variables in large datasets.
- Regression: Predicting a continuous value based on the relationships between variables.
- Anomaly Detection: Identifying unusual data points that do not fit the general pattern2 6 7.
Applications of Data Mining
Business and Market Analysis
Data mining has significant applications in business and market analysis. It helps organizations make crucial business decisions by extracting actionable information from large databases. This includes market analysis, business management, and decision support2 4.
Pharmaceutical Sector
In the pharmaceutical sector, data mining is used for signal detection to identify adverse drug reactions. This involves detecting anomalies in the data and determining if they are significant or random occurrences1.
Information-Providing Services
Data mining techniques are also applied in information-providing services, such as data warehousing and online services, to better understand user behavior, improve service quality, and increase business opportunities7.
Conclusion
Data mining is a powerful tool for discovering valuable patterns and knowledge from large datasets. By leveraging techniques from various disciplines, it enables organizations to make informed decisions and gain insights into complex data. As data continues to grow in volume and complexity, the importance of data mining in various fields, including business, healthcare, and information services, will only increase.
Sources and full results
Most relevant research papers on this topic
Principles of Data Mining
Data mining aims to discover valuable structures in large datasets, with applications in detecting adverse drug reactions in the pharmaceutical sector.
Highly CitedData Mining Techniques
Data mining is a process that discovers useful patterns in large amounts of data, aiding crucial business decisions.
Highly CitedData Mining
Data mining is an interdisciplinary science that aims to discover valuable structures in large data sets, but faces challenges in data quality and inference.
Highly Cited1. WHAT IS DATA MINING?
Data mining is the process of discovering interesting knowledge from large amounts of data stored in databases, data warehouses, or other information repositories.
Highly CitedDiscovering Knowledge in Data: An Introduction to Data Mining
Data mining is an interdisciplinary field that explores large databases to discover interesting relationships, using techniques from machine learning, pattern recognition, statistics, databases, and visualization.
Highly CitedThe Concept of Data Mining
Data mining is a technique for identifying patterns in large amounts of data, with benefits and drawbacks, and various methodologies and tasks for various applications.
Data Mining: An Overview from a Database Perspective
Data mining techniques can improve user experience, increase business opportunities, and enhance database systems.
Highly CitedData Mining Concepts and Techniques
Data mining is a collaborative effort between humans and computers, aiming to discover valuable and non-trivial information in large volumes of data.
Highly CitedData Mining: Data Mining Concepts and Techniques
Data mining is a complex process that involves various techniques and processes to extract useful information from large data sets and create understandable structures for future use.
Highly CitedPerspectives on Data Mining
Data mining has matured as a data analysis technology, with sophisticated commercial software packages available, and is now relevant in market research and statistical analysis.
Try another search
The safety and efficacy of online therapy platforms in mental health care.
What are the health implications of the global rise in antibiotic-resistant bacteria?
The role of animal agriculture in climate change and environmental degradation.
what are social benefits of consuming green products?
The role of artificial photosynthesis in renewable energy generation.
what animals are the closest to dinosaurs