Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Clustering of time-series subsequences is meaningless: implications for previous and future research
Knowledge and Information Systems
Clustering of time series data-a survey
Pattern Recognition
Temporal Data Mining
Data Mining and Knowledge Discovery Handbook
Data Mining and Knowledge Discovery Handbook
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In this paper, we focus on two aspects of time series mining: first on the transformation of numerical data to symbolic data; then on the search for frequent patterns in the resulting symbolic time series. We are thus interested in some patterns which have a high frequency in our database of time series and might help to generate candidates for various tasks in the area of time series mining. During the symbolization phase, we transform the numerical time series into a symbolic time series by i) splitting this latter into consecutive subsequences, ii) using a clustering algorithm to cluster these subsequences, each subsequence being then replaced by the name of its cluster to produce the symbolic time series. In the second phase, we use a sliding window to create a collection of transactions from the symbolic time series, then we use some algorithm for mining sequential pattern to find out some interesting motifs in the original time series. An example experiment based on environmental data is presented.