Data Mining for Design and Marketing
Features
- Examines the unification of marketing and design as a new application domain
- Reflects new research in subjective value creation, including how data mining and visualization are used in market dynamics
- Analyzes human society to discover patterns in the business market
- Presents communication mining tools for industrial designs
- Uses social data to discover and design products and services
- Covers traditional evaluation criteria, such as predictive accuracy and computation speed
- Explores applications in online market analysis systems, website design, global teamwork projects, database retrieval, and e-commerce
Summary
Data Mining for Design and Marketing shows how to design and integrate data mining tools into human thinking processes in order to make better business decisions, especially in designing and marketing products and systems.
The expert contributors discuss how data mining can identify valuable consumer patterns, which aid marketers and designers in detecting consumers’ needs. They also explore visualization tools based on the computational methods of data mining. Discourse analysis, chance discovery, knowledge discovery, formal concept analysis, and an adjacency matrix are just some of the novel approaches covered. The book explains how these methods can be applied to website design, the retrieval of scientific articles from a database, personalized e-commerce support tools, and more.
Through the techniques of data mining, this book demonstrates how to effectively design business processes and develop competitive products and services. By embracing data mining tools, businesses can better understand the behavior and needs of their customers.
Table of Contents
Sensing Values in Designing Products and Markets on Data Mining and Visualizations Yukio Ohsawa
Reframing the Data Mining Process David Bergner and Ozgur Eris
The Use of Online Market Analysis Systems to Achieve Competitive Advantage Lihua Zhao, Mark D. Uncles, and Gary Gregory
Finding Hierarchical Patterns in Large POS Data Using Historical Trees Takanobu Nakahara and Hiroyuki Morita
A Method to Search ARX Model Orders and Its Application to Sales Dynamics Analysis Kenta Fukata, Takashi Washio, Katsutoshi Yada, and Hiroshi Motoda
Data Mining for Improved Website Design and Enhanced Marketing Asem Omari
Discourse Analysis and Creativity Support for Concept Product Design Noriko Imafuji, Xavier Llora, and David E. Goldberg
Data Crystallization with Human Interactions Applied for Designing New Products Kenichi Horie, Yoshiharu Maeno, and Yukio Ohsawa
Improving and Applying Chance Discovery for Design Analysis Brett Bojduj
Mining for Influence Leaders in Global Teamwork Projects Renate Fruchter, Shubashri Swaminathan, Naohiro Matsumura, and Yukio Ohsawa
Analysis Framework for Knowledge Discovery Related to Persuasion Process Conversation Logs Wataru Sunayama and Katsutoshi Yada
Association Bundle-Based Market Basket Analysis Wenxue Huang, Milorad Krneta, Limin Lin, and Jianhong Wu
Formal Concept Analysis with Attribute Priorities Radim Belohlavek and Vilem Vychodil
Literature Categorization System for Automated Database Retrieval of Scientific Articles Based on Dedicated Taxonomy Lukáš Pichl, Manabu Suzuki, Masaki Murata, Daiji Kato, Izumi Murakami, and Akira Sasaki
A Data Mining Framework for Designing Personalized E-Commerce Support Tools Timothy Maciag, Dominik Slezak, Daryl H. Hepting, and Robert J. Hilderman
An Adjacency Matrix Approach for Extracting User Sentiments from Sentences Bin Shi and Kuiyu Chang
Visualizing RFID Tag Data in a Library for Detecting Latent Interest of Users Yukio Ohsawa, Takuma Hosoda, and Takeshi Ui
Appendix A: KeyGraph and Pictorial KeyGraph
Appendix B: A Maximal Cliques Enumeration Algorithm for MBA Transaction Data
Index
