"2017ch6 商务智能中的聚类分析方法及应用"

apter 6 of Principles and Applications of Business Intelligence covers the topic of clustering, also known as cluster analysis. Cluster analysis is a technique used in data mining and machine learning to group similar data points together. It aims to find patterns or structures within a data set that may not be immediately apparent. There are various methods of clustering that can be categorized into different groups. These include partitioning methods, hierarchical methods, density-based methods, and model-based methods. Each method has its own strengths and weaknesses, and the choice of method depends on the specific characteristics of the data and the goals of the analysis. Types of data in cluster analysis include numerical data, categorical data, ordinal data, and spatial data. Similarity between data points can be computed using various measures such as Euclidean distance, Manhattan distance, and cosine similarity. Partitioning methods involve dividing the data into distinct clusters based on certain criteria. Popular partitioning algorithms include K-means, K-medoids, and CLARA. Hierarchical methods, on the other hand, build a hierarchical tree of clusters by recursively merging or splitting clusters. Agglomerative and divisive clustering are two common hierarchical methods. A case study is presented to demonstrate the application of clustering in business intelligence. This real-world example shows how clustering can be used to identify customer segments and personalize marketing strategies. In summary, cluster analysis is a powerful tool in business intelligence that allows organizations to uncover hidden patterns in their data and make informed decisions. By understanding the principles and applications of clustering, businesses can gain valuable insights that can lead to improved performance and competitiveness.