by Sarah Mason
Data Mining is defined as the process of finding patterns for evaluating trends and significance in a dataset. This is associated with big data, but has value for large datasets too. Data Mining is a form of Machine Learning with various preferred methods depending on structure of the data and what is the scope for analysis. There are specialized tools and apps for mining data, but the spreadsheet has been updated to handle this task in its basic form such as Microsoft Excel functions including geocoding and regression. Preferred tools include Weka, SPSS, Sisense, R, and Python.
Advanced analysis with data mining has enabled targeted marketing by learning preferences from user data, created insights into the reasons why consumers behave the way they do by understanding patterns in recorded data. Recommendation engines are used on many shopping sites to suggest items to buy based on results from the engine based on scoring the insights from real-time analysis of consumer behavior. This is an example of data mining and its use and utility.
Another example is associating and creating a group based on shared characteristics of a set. By clustering similar applicants, and insurer can quickly assess rates and offerings for a prospective customers based on provided information for auto insurance based on data. If there is an existing pool of subscribers that have the same zip code, driving habits, car make and model the rate quote should be same as an applicant that can be associated with an existing group of subscribers.
Performance of algorithms to learn from data can vary. Selecting the best method for accuracy, precision, and time to complete means knowing the data and understanding the reason for the analysis.
Top 4 Methods for Data Mining
- regression — statistical processes for estimating the relationships between a dependent variable and one or more independent variables.
- cluster — grouping a set of objects in such a way that variables in the same group are more similar to each other than to those in other groups.
- classification — categorizing elements of data in dataset.
- anomaly detection — identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data.
Summary
Data mining methods are ways to find patterns in data to provide insight. This list are methods that produce results for large and big data to understand common questions for consumer habits, rate quotes, and more. There are many reasons for data mining and over ten methods. The classic use cases are in marketing and insurance for large and big data to generate insights for business. When working with data and needing to handle volume, finding patterns can be more reliable than quantitative results due to processing the volume. This can generate more valuable information by looking at faster and more reliable models to recognize best fit for applying insights to solutions.
