Co-occurring cluster mining for damage patterns analysis of a fuel cell

Authors:
Daiki Inaba;Ken-ichi Fukui;Kazuhisa Sato;Junichirou Mizusaki;Masayuki Numao
Affiliations:
Graduate School of Information Science and Technology, Osaka University, Japan;The Institute of Scientific and Industrial Research, Osaka University, Ibaraki, Osaka, Japan;Graduate School of Engineering, Tohoku University, Japan;Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, Japan;The Institute of Scientific and Industrial Research, Osaka University, Ibaraki, Osaka, Japan
Venue:
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Year:
2012

Citing 8
Cited 0

Automatic subspace clustering of high dimensional data for data mining applications

SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Self-Organizing Maps

Self-Organizing Maps
Temporal Rule Discovery for Time-Series Satellite Images and Integration with RDB

PKDD '01 Proceedings of the 5th 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
Bursty and hierarchical structure in streams

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Quantitative Frequent Itemsets Using Adaptive Density-Based Subspace Clustering

ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Batch kernel SOM and related Laplacian methods for social network analysis

Neurocomputing
Dynamic spectrum classification by kernel classifiers with divergence-based kernels and its applications to acoustic signals

International Journal of Knowledge Engineering and Soft Data Paradigms

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this study, we research the mechanical correlations among components of solid oxide fuel cell (SOFC) by analyzing the co-occurrence of acoustic emission (AE) events which are caused by damage. Then we propose a novel method for mining patterns from the numerical data such as AE. The proposed method extracts patterns of two clusters considering co-occurrence between clusters and similarity within each cluster at the same time. In addition, we utilize the dendrogram obtained from hierarchical clustering for reduction of the search space. We applied the proposed method to AE data, and the damage patterns which represent the main mechanical correlations were extracted. We can acquire novel knowledge about damage mechanism of SOFC from the results.