FIRE: interactive visual support for parameter space-driven rule mining

Authors:
Abhishek Mukherji;Xika Lin;Jason Whitehouse;Christopher R. Botaish;Elke A. Rundensteiner;Matthew O. Ward
Affiliations:
Worcester Polytechnic Institute, Worcester, MA, USA;Worcester Polytechnic Institute, Worcester, MA, USA;Worcester Polytechnic Institute, Worcester, MA, USA;Worcester Polytechnic Institute, Worcester, MA, USA;Worcester Polytechnic Institute, Worcester, MA, USA;Worcester Polytechnic Institute, Worcester, MA, USA
Venue:
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Year:
2013

Citing 14
Cited 0

Mining frequent patterns without candidate generation

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A New Approach to Online Generation of Association Rules

IEEE Transactions on Knowledge and Data Engineering
Using Condensed Representations for Interactive Association Rule Mining

PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Itemset Trees for Targeted Association Querying

IEEE Transactions on Knowledge and Data Engineering
A scalable association rule visualization towards displaying large amounts of knowledge

IV '07 Proceedings of the 11th International Conference Information Visualization
Online mining of fuzzy multidimensional weighted association rules

Applied Intelligence
The WEKA data mining software: an update

ACM SIGKDD Explorations Newsletter
A shared execution strategy for multiple pattern mining requests over streaming data

Proceedings of the VLDB Endowment
Tuning database configuration parameters with iTuned

Proceedings of the VLDB Endowment
Variance aware optimization of parameterized queries

Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
AssocExplorer: an association rule visualization system for exploratory data analysis

Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
PARAS: a parameter space framework for online association mining

Proceedings of the VLDB Endowment
PARAS: interactive parameter space exploration for association rule mining

Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data

Quantified Score

Hi-index	0.00

Visualization

Abstract

While significant strides have been made on efficient association rule mining, the usability of mining systems woefully lags behind. In particular, the usability of rule mining systems is limited by the lack of support for interactive exploration of the relationships among rule results produced with various parameter settings. Based on a novel parameter space-driven approach, our proposed Framework for Interactive Rule Exploration (FIRE) addresses the usability shortcoming. FIRE features innovative visual displays and effective interactions that enable analysts to conduct rule exploration at the speed of thought. Particularly, the parameter space view (PSpace) displays the distribution of rules produced for diverse parameter settings. This not only facilitates user parameter selection but also empowers analyst's to understand rule relationships in the parameter space context. Our user study with 22 subjects establishes the usability and effectiveness of the proposed features and interactions of FIRE using benchmark datasets. Overall, this research encompasses significant contributions at the intersection of data mining, knowledge management and visual analytics.