Abstract: A system and method for merging product information from a first hierarchy into a second hierarchy. A Naive Bayes classification model is generated using both text data and attribute (numerical) data pertaining to products in the second hierarchy. Then, products in the first hierarchy are placed into the second hierarchy in accordance with the model. Preferably, the placement of the products in the second hierarchy depends in part on their grouping in the first hierarchy, on the intuition that if two products were grouped together in the first hierarchy they have a higher likelihood of being grouped together in the second hierarchy as well.

Abstract: A method and system for presenting a group of objects in a ranking order. Objects are ranked according to user preferences by first observing the access order of a related group of objects in relation to a predetermined access hypothesis. A user preference model is then adapted to correspond to any deviations between the access order and the access hypothesis for the related group of objects. Next, object preferences are calculated for each of the objects to be ranked according to the preference model. The group of objects is then presented to the user in an order corresponding to the calculated object preferences. The preference model is adaptively updated, unbeknownst to the user, in the normal course of accessing the presented objects.

Abstract: A method and apparatus for mining text databases, employing sequential pattern phrase identification and shape queries, to discover trends. The method passes over a desired database using a dynamically generated shape query. Documents within the database are selected based on specific classifications and user defined partitions. Once a partition is specified, transaction IDs are assigned to the words in the text documents depending on their placement within each document. The transaction IDs encode both the position of each word within the document as well as representing sentence, paragraph, and section breaks, and are represented in one embodiment as long integers with the sentence boundaries. A maximum and minimum gap between words in the phrases and the minimum support all phrases must meet for the selected time period may be specified. A generalized sequential pattern method is used to generate those phrases in each partition that meet the minimum support threshold.

Abstract: A system and method for determining the significance of association rules which are mined from a dataset is provided. Predictive association rules may also be generated based on the significance of an association rule. The statistical significance of an association rule may be used to estimate the number of false discoveries in a dataset or to rank discovered association rules by statistical significance to permit the user of the system to view the most statistically significant association rules first.

Abstract: A method for discovering association rules in a database that employs item constraints for extracting desired data relationships from a data base, thereby reducing the execution time of the rule discovery process and increasing the quality of the information returned. Such constraints allow users to specify the subset of rules in which the users are interested. Given a set of transactions D and constraints represented by a boolean expression .beta., the invention integrates the constraints into a selected rule discovery method rather than implementing the constraints as a post-processing step. The invention quickly discovers association rules that satisfy .beta. and have support and confidence levels greater than or equal to user-specified minimum support and minimum confidence levels, and may be implemented even when a taxonomy is present.

Abstract: A method and apparatus for mining text databases, employing sequential pattern phrase identification and shape queries, to discover trends. The method passes over a desired database using a dynamically generated shape query. Documents within the database are selected based on specific classifications and user defined partitions. Once a partition is specified, transaction IDs are assigned to the words in the text documents depending on their placement within each document. The transaction IDs encode both the position of each word within the document as well as representing sentence, paragraph, and section breaks, and are represented in one embodiment as long integers with the sentence boundaries. A maximum and minimum gap between words in the phrases and the minimum support all phrases must meet for the selected time period may be specified. A generalized sequential pattern method is used to generate those phrases in each partition that meet the minimum support threshold.

Abstract: A method and system are disclosed for performing spatial similarity joins on high-dimensional points that represent data objects of a database. The method comprises the steps of: generating a data structure based on the similarity distance .epsilon. for organizing the high-dimensional points, traversing the data structure to select pairs of leaf nodes from which the high-dimensional points are joined, and joining the points from selected pairs of nodes according to a joining condition based on the similarity distance .epsilon.. An efficient data structure referred to as an .epsilon.-K-D-B tree is disclosed to provide fast access to the high-dimensional points and to minimize system storage requirements. The invention provides algorithms for generating the .epsilon.-K-D-B tree using biased splitting to minimize the number of nodes to be examined during join operations. The traversing step includes joining selected pairs of nodes and also self-joining selected nodes.

Abstract: A system and method for mining databases includes a computer-implemented program which identifies patterns of transaction sequences that are stored in a database and which recur in the database with a user-defined regularity. The invention first identifies which sequences are large, i.e., which recur with the defined regularity, and then determines which sequences are maximal, i.e., which large sequences are not subsets of other large sequences. The set of maximal large sequences is returned to the user to indicate recurring purchasing patterns over time.

Abstract: A method for performing a range-sum query in a database, in which the data is represented as a multi-dimensional data cube, is disclosed. The method comprises the steps of: selecting a subset of the dimensions of the data cube; computing a set of prefix-sums along the selected dimensions, based on the aggregate values in the cube corresponding the queried ranges; and generating a range-sum result based on the computed prefix-sums. Two d-dimensional arrays A and P are used for representing the data cube and the prefix-sums of the data cube, respectively. By maintaining the prefix-sum array P of the same size as the data cube, all range queries for a given cube can be answered in constant time, irrespective of the size of the sub-cube circumscribed by a query, using the inverse binary operator of the SUM operator. Alternatively, only auxiliary information for any user-specified fraction of the size of the d-dimensional data cube is maintained, to minimize the required system storage.

Abstract: A system and method for discovering consumer purchasing tendencies includes a computer-implemented program which identifies consumer transaction itemsets that are stored in a database and which appear in the database a user-defined minimum number of times, referred to as minimum support. Then, the system discovers association rules in the itemsets by comparing the ratio of the number of times each of the large itemsets appears in the database to the number of times particular subsets of the itemset appear in the database. When the ratio exceeds a predetermined minimum confidence value, the system outputs an association rule which is representative of purchasing tendencies of consumers.

Abstract: A method and apparatus are disclosed for mining generalized sequential patterns from a large database of data sequences, taking into account user specified constraints on the time-gap between adjacent elements of the patterns, sliding time-window, and taxonomies over data items. The invention first identifies the items with at least a minimum support, i.e., those contained in more than a minimum number of data sequences. The items are used as a seed set to generate candidate sequences. Next, the support of the candidate sequences are counted. The invention then identifies those candidate sequences that are frequent, i.e., those with a support above the minimum support. The frequent candidate sequences are entered into the set of sequential patterns, and are used to generate the next group of candidate sequences.

Abstract: A method and apparatus are disclosed for mining quantitative association rules from a relational table of records. The method comprises the steps of: partitioning the values of selected quantitative attributes into intervals, combining adjacent attribute values and intervals into ranges, generating candidate itemsets, determining frequent itemsets, and outputting an association rule when the support for a frequent itemset bears a predetermined relationship to the support for a subset of the frequent itemset. Preferably, the partitioning step includes determining whether to partition and the number of partitions based on a partial incompleteness measure. The candidate generation includes discarding those itemsets not meeting a user-specified interest level and those having a subset which is not a frequent itemset. The frequent itemsets are determined using super-candidates that include information of the candidate itemsets.

Abstract: A system and method for discovering consumer purchasing tendencies includes a computer-implemented program which identifies consumer transaction itemsets that are stored in a database and which appear in the database a user-defined minimum number of times, referred to as minimum support. The itemsets contain items that are characterized by a hierarchical taxonomy. Then, the system discovers association rules, potentially across different levels of the taxonomy, in the itemsets by comparing the number of times each of the large itemsets appears in the database to the number of times particular subsets of the itemset appear in the database. When the relationship exceeds a predetermined minimum confidence value, the system outputs a generalized association rule which is representative of purchasing tendencies of consumers. The set of generalized association rules can be pruned of uninteresting rules, i.e.