Abstract: Techniques for the implantation of time-dependent features (e.g., slope features) in existing data analysis models are disclosed. Time-dependent features are applied in machine learning algorithms to provide deeper analysis of temporally spaced data. Temporally spaced data is time-based or time-dependent data where data is populated at different points in time over some period of time. Implementing the time-dependent features enables application of first derivatives that define slopes over time (e.g., performance) windows within the period of time of the data. Application of the first derivatives provides analysis of the trend of the data over time. Additional features and/or higher order derivatives may also be applied to the first derivatives to provide further refinement to analysis of the temporally spaced data.

Abstract: Methods and systems are presented for mining data in association with predicting occurrences of events. Upon detecting an occurrence of an event associated with a transaction, a data mining system accesses data associated with different transactions, and generates a decision tree for predicting occurrences of the event based on the data. Using a classification specification, the data mining system traverses the decision tree and prunes at least a portion of the decision tree that does not satisfy the classification specification. The data mining system then extracts data relevant to predicting occurrences of the event from the pruned decision tree. The extracted data includes attributes and/or criteria that are relevant to predicting occurrences of the event. Based on the extracted data, one or more actions can be performed to improve the event prediction process and/or reduce the frequency of the occurrences of the event.