One area of DM that is attracting a good amount of attention is distributed and collective DM. Much of the DM that is being done currently focuses on a DB or data warehouse of information that is physically located in one place. However, the situation arises where information may be located in different places, or in different physical locations. This is known generally as distributed-data mining (DDM). Therefore, the goal is to effectively mine distributed data that are located in heterogeneous sites. DDM is used to offer a different approach to traditional approaches to analysis, by using a combination of localized data analysis together with a "global data model." In more specific terms, this is defined as performing local data analysis for generating partial data models, and combining the local data models from different data sites in order to develop the global model. This global model combines the results of the separate analyses. Often the global model produced may become incorrect or ambiguous, especially if the data in different locations have different features or characteristics. This problem is especially critical when the data in distributed sites are heterogeneous rather than homogeneous. These heterogeneous data sets are known as vertically partitioned data sets. An approach proposed by Kargupta, Park, Herschberger, and Johnson (2000) speaks of the collective data-mining (CDM) approach, which provides a better approach to vertically partitioned data sets using the notion of orthonormal basis functions, and computes the basis coefficients to generate the global model of the data.