Data mining parameters
Clustering parameters find and visually document groups of facts that were previously unknown. Clustering groups a set of objects and aggregates them based on how similar they are to each other.
There are different ways a user can implement the cluster, which differentiate between each clustering model. Fostering parameters within data mining can discover patterns in data that can lead to reasonable predictions about the future, also known as predictive analysis.
Data mining tools and techniques
Data mining techniques are used in many research areas, including mathematics, cybernetics, genetics and marketing. While data mining techniques are a means to drive efficiencies and predict customer behavior, if used correctly, a business can set itself apart from its competition through the use of predictive analysis.
Web mining, a type of data mining used in customer relationship management, integrates information gathered by traditional data mining methods and techniques over the web. Web mining aims to understand customer behavior and to evaluate how effective a particular website is.
Other data mining techniques include network approaches based on multitask learning for classifying patterns, ensuring parallel and scalable execution of data mining algorithms, the mining of large databases, the handling of relational and complex data types, and machine learning. Machine learning is a type of data mining tool that designs specific algorithms from which to learn and predict.