Mining patterns from longitudinal studies

Authors:
Aída Jiménez;Fernando Berzal;Juan-Carlos Cubero
Affiliations:
Department of Computer Science and Artificial Intelligence, CITIC, University of Granada, Granada, Spain;Department of Computer Science and Artificial Intelligence, CITIC, University of Granada, Granada, Spain;Department of Computer Science and Artificial Intelligence, CITIC, University of Granada, Granada, Spain
Venue:
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Year:
2011

Citing 13
Cited 0

Mining frequent patterns without candidate generation

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A longitudinal study of World Wide Web users' information-searching behavior

Journal of the American Society for Information Science and Technology
Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Efficient Data Mining for Maximal Frequent Subtrees

ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
HybridTreeMiner: An Efficient Algorithm for Mining Frequent Rooted Trees and Free Trees Using Canonical Forms

SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
Efficiently Mining Frequent Trees in a Forest: Algorithms and Applications

IEEE Transactions on Knowledge and Data Engineering
AMIOT: Induced Ordered Tree Mining in Tree-Structured Databases

ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Efficiently Mining Frequent Embedded Unordered Trees

Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Longitudinal study of people learning to use continuous voice-based cursor control

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Longitudinal nominal data analysis using marginalized models

Computational Statistics & Data Analysis
POTMiner: mining ordered, unordered, and partially-ordered trees

Knowledge and Information Systems
Frequent tree pattern mining: A survey

Intelligent Data Analysis
Using trees to mine multirelational databases

Data Mining and Knowledge Discovery

Quantified Score

Hi-index	0.00

Visualization

Abstract

Longitudinal studies are observational studies that involve repeated observations of the same variables over long periods of time. In this paper, we propose the use of tree pattern mining techniques to discover potentially interesting patterns within longitudinal data sets. Following the approach described in [15], we propose four different representation schemes for longitudinal studies and we analyze the kinds of patterns that can be identified using each one of the proposed representation schemes. Our analysis provides some practical guidelines that might be useful in practice for exploring longitudinal datasets.