Skip to main navigation
Skip to search
Skip to main content
Penn State Home
Help & FAQ
Link opens in a new tab
Search content at Penn State
Home
Researchers
Research output
Research units
Equipment
Grants & Projects
Prizes
Activities
Trend mining for predictive product design
Conrad S. Tucker
, Harrison M. Kim
School of Engineering Design and Innovation
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
2
Link opens in a new tab
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Trend mining for predictive product design'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Product Design
100%
Trend Mining
100%
Modeling Techniques
66%
Design Engineers
66%
Consumer Preferences
66%
Product Features
66%
Demand Modeling
66%
Modeling Approach
33%
Data Mining Techniques
33%
Product Architecture
33%
Cell Phone
33%
Detection Method
33%
Demand Model
33%
Predictive Models
33%
Fundamental Challenges
33%
Prediction Accuracy
33%
Future Demand
33%
Classification Scheme
33%
Model Generation
33%
Predictive Power
33%
Trend Pattern
33%
Predictive Modeling
33%
Product Preference
33%
Multi-generation Products
33%
Trend Detection
33%
Mining Model
33%
Novel Classification
33%
Irrelevance
33%
Entropy Value
33%
Timestamp Data
33%
Design Community
33%
Time-frequency Entropy
33%
Exponential Smoothing
33%
Statistical Trends
33%
Computer Science
Product Design
100%
Design Engineer
66%
Product Feature
66%
Product Architecture
33%
Cell Phone
33%
Predictive Model
33%
Classification Scheme
33%
Predictive Accuracy
33%
Exponential Smoothing
33%
Model Generation
33%
Predictive Power
33%
Engineering
Product Design
100%
Design Engineer
66%
Cellphone
33%
Multistage
33%
Detection Technique
33%
Current Demand
33%
Classification Scheme
33%