Data Mining for Design and MarketingYukio Ohsawa, Katsutoshi Yada CRC Press, 2009 M01 26 - 336 pages 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 de |
From inside the book
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Page 6
... for making the whole system run collaboratively. In other words, looking over the variety of Speed of image sampling “line-like” “spottiness” “bubbles” Image processing software 6 Data Mining for Design and Marketing.
... for making the whole system run collaboratively. In other words, looking over the variety of Speed of image sampling “line-like” “spottiness” “bubbles” Image processing software 6 Data Mining for Design and Marketing.
Page 7
... word by a black node, the co-occurrence of these frequent words in the same reports by black lines—cluster of (1) mentioned in this section—and rare words co-occurring with words in multiple clusters—items of (2)—the overall structure ...
... word by a black node, the co-occurrence of these frequent words in the same reports by black lines—cluster of (1) mentioned in this section—and rare words co-occurring with words in multiple clusters—items of (2)—the overall structure ...
Page 8
... words, eye tracks, etc for metacognition and new events Interaction with environment Nextcycle Actions Visualize scenario maps and rules from collected data “20$ cheese is not a FSI, but is important!” “ch eese, b read -> bu tter ...
... words, eye tracks, etc for metacognition and new events Interaction with environment Nextcycle Actions Visualize scenario maps and rules from collected data “20$ cheese is not a FSI, but is important!” “ch eese, b read -> bu tter ...
Page 9
... words were typed into KeyGraph during communications and were visualized on their co-occurrences in the same sentences as in the center of Figure 1.6. Objective data from the real world or (not exclusive) subjective data from their ...
... words were typed into KeyGraph during communications and were visualized on their co-occurrences in the same sentences as in the center of Figure 1.6. Objective data from the real world or (not exclusive) subjective data from their ...
Page 11
... words may stimulate meta-cognition and accelerate the spiral process of chance discovery. 1.6 gap between advertisers and designers: results of visualizing messages Throughout this section, we focus attention on people who make a living ...
... words may stimulate meta-cognition and accelerate the spiral process of chance discovery. 1.6 gap between advertisers and designers: results of visualizing messages Throughout this section, we focus attention on people who make a living ...
Contents
1 | |
19 | |
Chapter 3 The Use of Online Market Analysis Systems to Achieve Competitive Advantage | 35 |
Chapter 4 Finding Hierarchical Patterns in Large POS Data Using Historical Trees | 57 |
Chapter 5 A Method to Search ARX Model Orders and Its Application to Sales Dynamics Analysis | 81 |
Chapter 6 Data Mining for Improved Web Site Design and Enhanced Marketing | 95 |
Chapter 7 Discourse Analysis and Creativity Support for Concept Product Design | 107 |
Chapter 8 Data Crystallization with Human Interactions Applied for Designing New Products | 119 |
Chapter 12 Association Bundle Based Market Basket Analysis | 187 |
Chapter 13 Formal Concept Analysis with Attribute Priorities | 211 |
Chapter 14 Literature Categorization System for Automated Database Retrieval of Scientific Articles Based on Dedicated Taxonomy | 223 |
Chapter 15 A DataMining Framework for Designing Personalized ECommerce Support Tools | 235 |
Chapter 16 An Adjacency Matrix Approach for Extracting User Sentiments | 251 |
Chapter 17 Visualizing RFID Tag Data in a Library for Detecting Latent Interest of Users | 277 |
KeyGraph and Pictorial KeyGraph | 295 |
A Maximal Cliques Enumeration Algorithm for MBA Transaction Data | 299 |
Chapter 9 Improving and Applying Chance Discovery for Design Analysis | 137 |
Chapter 10 Mining for Influence Leaders in Global Teamwork Projects | 149 |
Chapter 11 Analysis Framework for Knowledge Discovery Related to Persuasion Process Conversation Logs | 171 |
Index | 307 |
Back cover | 321 |
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Common terms and phrases
adjacency matrix applications approach association rules atomic unit attributes cell phone chance discovery cheese clusters complete homogeneity-based association Computer concept lattice consumers CRSA customers DAIC data crystallization data mining data set data-mining data-mining process database decision makers develop discussion DRSA dynamic Engineering evaluation example extracted formal concept Formal Concept Analysis frame framework frequent item sets graph mining homogeneity-based association bundle improve influence leader influence topic input interface KeyGraph knowledge label late payer machine learning market analysis Matrix maximal clique message feature model order MOEA Node graph Node vertex Ohsawa OMASs pair homogeneity-based association participants pattern mining patterns performance Pictograms purchasing Research RFID tags rough set scenario map segment sentence spatial structure subset Table team members technologies ThinkTank ThinkTank-IDM threshold tion Unigram visualization weighted association strength words