The parallel data mining method is an effective approach for accurate marketing that involves building client value and behavior models, classifying clients based on these models, and offering preference services to high-potential clients. This research explores the parallel data mining method and its application to identify mobile client bases, understand client preferences, and accurately judge client social group classes. The study uses parallel clustering and classification algorithms to cluster and classify clients rapidly, enabling enterprises to formulate different strategies for different client bases. By applying this method, an enterprise can achieve profit maximization and an important guiding function for the business.

parallel data mining, client value model, client behavior model, preference services, clustering algorithm, classification algorithm, profit maximization, accurate marketing.