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Recently, with companies and government agencies saving large repositories of time stream/temporal data, there is a large push for adapting association rule mining algorithms for dynamic, targeted querying. In addition, issues with data processing latency and results depreciating in value with the passage of time, create a need for swifter and more efficient processing. The aim of targeted association mining is to find potentially interesting implications in large repositories of data. Using targeted association mining techniques, specific implications that contain items of user interest can be found faster and before the implications have depreciated in value beyond usefulness.In this paper, the DynTARM algorithm is proposed for the discovery of targeted and rare association rules. DynTARM has the flexibility to discover strong and rare association rules from data streams within the user's sphere of interest. By introducing a measure, called the Volatility Index, to assess the fluctuation in the confidence of rules, rules conforming to different temporal patterns are discovered.

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