Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
On the Computation of Multidimensional Aggregates
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Mining Motifs in Massive Time Series Databases
ICDM '02 Proceedings of the 2002 IEEE International Conference on 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
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Probabilistic discovery of time series motifs
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Towards parameter-free data mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Visually mining and monitoring massive time series
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Assumption-free anomaly detection in time series
SSDBM'2005 Proceedings of the 17th international conference on Scientific and statistical database management
Intelligent Icons: Integrating Lite-Weight Data Mining and Visualization into GUI Operating Systems
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
IV '07 Proceedings of the 11th International Conference Information Visualization
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
A novel bit level time series representation with implication of similarity search and clustering
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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Data Mining is the process of discovering potentially valuable patterns, associations, trends, sequences and dependencies in data. Data mining techniques can discover information that many traditional business analysis and statistical techniques fail to deliver. In our study, we emphasis on the use of data mining techniques on time series, where mining techniques and tools are used in an attempt to recognize, anticipate and learn the time series behavior with different directly related or looked unrelated factors. Targeted data are sequences of observations collected over intervals of time. Each sequence describes a phenomenon or a factor. Such factors could have either a direct or indirect impact on the time series under study. Examples of factors with direct impact include the yearly budgets and expenditures, taxations, local stocks prices, unemployment rates, inflation rates, fallen angels, and rising odds for upgrades. Indirect factors could include any phenomena in the local or global environments, such as, global stocks prices, education expenditures, weather conditions, employment strategies, and medical services. Analysis on data includes discovering trends (or patterns) and association between sequences in order to generate non-trivial knowledge. In this paper, we propose a data mining technique to predict the dependency between factors that affect performance. The proposed technique consists of three phases: (a) for each data sequence that represents a chosen phenomenon, generate its trend sequences, (b) discover maximal frequent trend patterns, generate pattern vectors (to keep information of frequent trend patterns), use trend pattern vectors to predict future factor sequences.