Titlebook: Information Granularity, Big Data, and Computational Intelligence; Witold Pedrycz,Shyi-Ming Chen Book 2015 Springer International Publishi

熟練

Incrementally Mining Frequent Patterns from Large Database generate a large tree structure. In this paper, we propose two efficient algorithms which only keep frequent items in a condensed tree structure. When a set of new transactions is added into the database, our algorithms can efficiently update the tree structure without scanning the original database.

violate

Improved Latent Semantic Indexing-Based Data Mining Methods and an Application to Big Data Analysis that in the context of customer support centers, service experience has strongly influence on perceived customer satisfaction and service quality. Based on the research results an improved approach for innovative CRM is presented. The thesis proposes three methods and explains an application to big data analysis for CRM at the end.

certitude

Multi-granular Evaluation Model Through Fuzzy Random Regression to Improve Information Granularityction process for fruits requires a method to ensure product quality. We include simulation results and highlight the advantage of the proposed method in handling the existence of fuzzy random information.

無意

種屬關(guān)系

Filibuster

Customer Relationship Management and Big Data Miningcs, increase the value to the customer, and improve their competitive advantages of enterprises. In this chapter, discuss big data mining, customer relationship management, customer value, and propose a case study of big data mining for customer relationship management with data of the Automotive Maintenance Industry.

外貌

心神不寧

The Web KnowARR Framework: Orchestrating Computational Intelligence with Graph Databases On one hand, they resort to non-public content and on the other they resort to content that is available to the public (mostly on the Web). The Semantic Web offers opportunities not only to present public content descriptively but also to show relationships. The proposed framework can serve as the basis for the public content of stakeholder maps.

指數(shù)

LAITY