There are many ways to conduct segmentation. There are various methodologies that one can use to influence how small, big, or distinct the cluster constituents are from each other. Without delving much into the methodology, let's first look at what we mean by the distance between clusters and how it impacts on the results. In Figure 7.10, we have four imaginary lines, L1-L4, which we will use to understand the distance between clusters. The x-axis shows the customer IDs and the y-axis shows the distance between cluster centroids. The higher the distance, the further the clusters are from each other. At L1, we can see that we don't have a relationship between dissimilar customers. Customers 5 and 6 and 2, 3 have matching values and hence they are paired together, but they don't form a cluster with any other (duplicate/non-matching), customers...
SAS for Finance
By :
SAS for Finance
By:
Overview of this book
SAS is a groundbreaking tool for advanced predictive and statistical analytics used by top banks and financial corporations to establish insights from their financial data.
SAS for Finance offers you the opportunity to leverage the power of SAS analytics in redefining your data. Packed with real-world examples from leading financial institutions, the author discusses statistical models using time series data to resolve business issues.
This book shows you how to exploit the capabilities of this high-powered package to create clean, accurate financial models. You can easily assess the pros and cons of models to suit your unique business needs.
By the end of this book, you will be able to leverage the true power of SAS to design and develop accurate analytical models to gain deeper insights into your financial data.
Table of Contents (9 chapters)
Preface
Free Chapter
Time Series Modeling in the Financial Industry
Forecasting Stock Prices and Portfolio Decisions using Time Series
Credit Risk Management
Budget and Demand Forecasting
Inflation Forecasting for Financial Planning
Managing Customer Loyalty Using Time Series Data
Transforming Time Series – Market Basket and Clustering
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