Periodic Pattern Mining, an interdisciplinary field of data mining is concerned with analyzing large volumes of time series or temporal data to discover patterns or trends or certain characteristics of data automatically. Temporal data captures the evolution of a data value over time. The existing Periodicity Mining Process is Text-Based which can be applied only to text data. The project proposed deals with the Periodic Patterns in Multimedia Data which includes text as well as audio and images. Multimedia data such as digital images and audio can be treated as temporal values, since a timestamp is implicitly attached to every instant of the signal. A Cross Correlation based approach is proposed for periodic mining of multimedia data which has its main application in pattern recognition. In multimedia data mining, when the same signal is compared to phase shifted copies of itself, the procedure is known as autocorrelation Basically Cross Correlation is a mathematical tool for finding repeating patterns in periodic signals by analyzing the degree of similarity between them. The periodic pattern retrieved from text data has its application in prediction, forecasting and detection of anomalies or unusual activities. The patterns extracted from audio and image finds its application in content based retrieval, compression and segmentation.

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Auto-correlation Cross Correlation Compression Content Based Retrieval Periodic Pattern Mining Segmentation Time Series Data