Abstract: An electronic data mining process for mining from an electronic data base using an electronic digital computer a listing of commercially useful information of the type known in the art as an association rule containing at least one uninstantiated condition. For example, the commercially useful information may be information useful for sales promotion, such as promotion of telephone usage. The computer retrieves from the database a plurality of stored parameters from which measures of the uninstatiated condition can be determined. The computer uses a dynamic programming algorithm and iterates over intervals or sub-ranges of the parameters to obtain what is called an at least partially optimized association rule, as optimized intervals or sub-ranges of at least some of the retrieved parameters, for example, time intervals of high usage of certain types of telephone connections. These optimized intervals are provided as the listed commercially useful information.

Abstract: A new method for identifying a predetermined number of data points of interest in a large data set. The data points of interest are ranked in relation to the distance to their neighboring points. The method employs partition-based detection algorithms to partition the data points and then compute upper and lower bounds for each partition. These bounds are then used to eliminate those partitions that do contain the predetermined number of data points of interest. The data points of interest are then computed from the remaining partitions that were not eliminated. The present method eliminates a significant number of data points from consideration as the points of interest, thereby resulting in substantial savings in computational expense compared to conventional methods employed to identify such points.

Abstract: Data mining systems and methods are disclosed for associating users with items based on underlying personas. The system associates each user account with one or more underlying personas that contribute to the user's interactions with different items, and predicts an active persona for a user based on the user's recent interactions with items and make item related recommendations that are oriented to the active persona. Thus, for example, even though multiple individuals may share a computer and/or account, the content (e.g., item recommendations) presented during a browsing session may be based primarily or exclusively on the past browsing behaviors of the particular individual conducting the browsing session.