Statistical measures in large database , Statistical – based algorithms , Distance based algorithms

In data mining, there are three types of statistical techniques that are commonly used in analyzing large databases: statistical measures, statistical-based algorithms, and distance-based algorithms.

Statistical Measures: Statistical measures are used to describe and summarize the data in a dataset. Some common statistical measures used in data mining include mean, median, mode, variance, standard deviation, and correlation. These measures can help in identifying patterns, trends, and relationships within the data.

Statistical-based Algorithms: Statistical-based algorithms use statistical models to analyze the data and uncover patterns. These algorithms are used in supervised learning, where the algorithm is trained on a labeled dataset to make predictions on new, unlabeled data. Some examples of statistical-based algorithms include linear regression, logistic regression, and Naive Bayes.

Distance-based Algorithms: Distance-based algorithms are used to identify clusters or groups within a dataset based on the similarity or dissimilarity between the data points. These algorithms use distance metrics, such as Euclidean distance or cosine distance, to measure the similarity or dissimilarity between the data points. Some common distance-based algorithms include k-means clustering, hierarchical clustering, and density-based clustering.

Overall, statistical techniques are essential in data mining for describing, summarizing, and analyzing large databases. By using these techniques, businesses can identify patterns and relationships within the data that can be used to make informed decisions and improve their operations.