Data mining is the process of sorting through large data sets to identify patterns and establish relationships to solve problems through data analysis. Data mining tools allow enterprises to predict future trends.Content Continues Below
Data mining parameters
In data mining, association rules are created by analyzing data for frequent if/then patterns, then using the support and confidence criteria to locate the most important relationships within the data. Support is how frequently the items appear in the database, while confidence is the number of times if/then statements are accurate.
Other data mining parameters include Sequence or Path Analysis, Classification, Clustering and Forecasting. Sequence or Path Analysis parameters look for patterns where one event leads to another later event. A Sequence is an ordered list of sets of items, and it is a common type of data structure found in many databases. A Classification parameter looks for new patterns, and might result in a change in the way the data is organized. Classification algorithms predict variables based on other factors within the database.
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.
Benefits of data mining
In general, the benefits of data mining come from the ability to uncover hidden patterns and relationships in data that can be used to make predictions that impact businesses.
Specific data mining benefits vary depending on the goal and the industry. Sales and marketing departments can mine customer data to improve lead conversion rates or to create one-to-one marketing campaigns. Data mining information on historical sales patterns and customer behaviors can be used to build prediction models for future sales, new products and services.
Companies in the financial industry use data mining tools to build risk models and detect fraud. The manufacturing industry uses data mining tools to improve product safety, identify quality issues, manage the supply chain and improve operations.
IBM SPSS predictive analytics tools for big data may be the best option for your enterprise. KNIME open source data analytics delivers commercial extensions for big data, cluster operations and collaboration.
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