Data Mining in Crystallography

More about the book

Humans have been extracting patterns from data for centuries, but the growing volume of data today necessitates more automated approaches. Early methods like Bayes’ theorem and Regression analysis laid the groundwork, while advancements in computer technology have significantly enhanced data collection and storage capabilities. As data sets become larger and more complex, hands-on analysis has increasingly been supplemented by automatic data processing. Data mining has emerged as a key tool for uncovering hidden patterns, leveraging computing power and innovative methodologies for knowledge discovery. This evolution has been supported by developments in computer science, including Neural networks, Clustering, Genetic algorithms, Decision trees, and Support vector machines. Data mining typically encompasses four main tasks: 1. **Classification**: Organizes data into predefined groups, such as distinguishing legitimate emails from spam using algorithms like Nearest neighbor and Naive Bayes. 2. **Clustering**: Similar to classification, but without predefined groups, allowing the algorithm to identify and group similar items. 3. **Regression**: Seeks to model data with minimal error, often employing Genetic Programming. 4. **Association rule learning**: Investigates relationships between variables, such as analyzing customer purchase data in a supermarket.