"数据挖掘之Cluster Analysis教程：分类方法与应用"

Cluster analysis, also known as clustering, is a critical technique in data mining that involves grouping data into classes or clusters based on similarities and dissimilarities within the data. The process of cluster analysis aims to identify patterns and relationships within the data, making it easier to understand and interpret large datasets. There are various types of data that can be analyzed using cluster analysis, including numerical data, categorical data, and mixed data types. Different clustering methods are used depending on the type of data being analyzed, with each method having its strengths and weaknesses. Major clustering methods can be categorized into hierarchical clustering, partitioning methods, density-based clustering, model-based clustering, and grid-based clustering. Each method has its own approach to grouping data and may be more suitable for certain types of data or specific analytical objectives. Some typical clustering methods include K-means clustering, hierarchical clustering, DBSCAN, and expectation-maximization clustering. These methods use different algorithms and techniques to identify clusters within the data and can be applied to various datasets and analytical tasks. In addition to clustering, outlier analysis is another important aspect of cluster analysis that involves identifying and handling outliers in the data. Outliers are data points that significantly deviate from the rest of the data and can distort the clustering results if not properly addressed. Overall, cluster analysis is a powerful data mining technique that enables researchers and analysts to uncover hidden patterns and relationships within large datasets. By using clustering methods and outlier analysis, analysts can gain valuable insights into the data and make informed decisions based on the patterns identified.