Temporal databases: theory, design, and implementation
Temporal databases: theory, design, and implementation
Efficient parallel data mining for association rules
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
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
Peculiarity Oriented Multi-database Mining
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Identifying Relevant Databases for Multidatabase Mining
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Mining Asynchronous Periodic Patterns in Time Series Data
IEEE Transactions on Knowledge and Data Engineering
Peculiarity Oriented Multidatabase Mining
IEEE Transactions on Knowledge and Data Engineering
Mining Dynamic Interdimension Association Rules for Local-Scale Weather Prediction
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Workshops and Fast Abstracts - Volume 02
SMCA: A General Model for Mining Asynchronous Periodic Patterns in Temporal Databases
IEEE Transactions on Knowledge and Data Engineering
An Architecture for Efficient Hardware Data Mining using Reconfigurable Computing Systems
FCCM '06 Proceedings of the 14th Annual IEEE Symposium on Field-Programmable Custom Computing Machines
Discovery of Periodic Patterns in Spatiotemporal Sequences
IEEE Transactions on Knowledge and Data Engineering
Efficient Similarity Search over Future Stream Time Series
IEEE Transactions on Knowledge and Data Engineering
A Framework for Mining Sequential Patterns from Spatio-Temporal Event Data Sets
IEEE Transactions on Knowledge and Data Engineering
Autoregressive-model-based missing value estimation for DNA microarray time series data
IEEE Transactions on Information Technology in Biomedicine
Discovering Transitional Patterns and Their Significant Milestones in Transaction Databases
IEEE Transactions on Knowledge and Data Engineering
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Data mining is concerned with analyzing large volumes of unstructured data to discover interesting regularities or relationships which in turn lead to better understanding of the underlying processes. Existing algorithms like association rule mining, incremental mining and frequent pattern mining can be used to find out valid periodic pattern and it can't be used to find out peculiar data. In this paper, two algorithms namely Peculiarity factor algorithm and Chi-Square test algorithm are used to find out peculiar data from a temporal database which is presented in vertical format. If peculiar data are found in two different relations there is need to use a value in a key as the relevance factor in order to find out the relevance between those relations. Thus a new dataset is formed from an existing dataset after the removal of peculiar data. From a new dataset Periodic Patterns were found by applying four phase algorithms namely singular periodic pattern mining, multi-event periodic pattern mining, complex periodic pattern mining and asynchronous sequence mining. Our proposed work focuses on prediction of time series data. This can be done with the help of correlation estimation. After determining strong and weak attributes using correlation estimation only strong attributes are considered to find out how each attribute is correlated with other attributes. Based on the correlation we predicted the required attribute values under given test conditions. Based on the prediction output precision and recall are calculated and hence accuracy is measured. Experimental results on real-life datasets demonstrate that the proposed algorithm is effective and efficient to predict the time series data.