Scalable system for expectation maximization clustering of large databases
Patent 6263337 Issued on July 17, 2001. Estimated Expiration Date: 
May 22, 2018. Estimated Expiration Date is calculated based on simple USPTO term provisions. It does not account for terminal disclaimers, term adjustments, failure to pay maintenance fees, or other factors which might affect the term of a patent.
May 22, 2018. Estimated Expiration Date is calculated based on simple USPTO term provisions. It does not account for terminal disclaimers, term adjustments, failure to pay maintenance fees, or other factors which might affect the term of a patent.Patent ReferencesAutomatic clustering method Method and system for data clustering for very large databases Method for mining causality rules with applications to electronic commerce Object-oriented data mining and decision making system System and method for data mining from relational data by sieving through iterated relational reinforcement Patent #: 5884305 InventorsAssigneeApplicationNo. 083906 filed on 05/22/1998US Classes:707/6, Pattern matching access707/3, Query processing (i.e., searching)707/4Query formulation, input preparation, or translationExaminersPrimary: Amsbury, WayneAssistant: Havan, Thu-Thao Attorney, Agent or FirmInternational ClassG06F 017/00AbstractIn one exemplary embodiment the invention provides a data mining system for use in finding clusters of data items in a database or any other data storage medium. Before the data evaluation begins a choice is made of the number M of models to be explored, and the number of clusters (K) of clusters within each of the M models. The clusters are used in categorizing the data in the database into K different clusters within each model. An initial set of estimates for a data distribution of each model to be explored is provided. Then a portion of the data in the database is read from a storage medium and brought into a rapid access memory buffer whose size is determined by the user or operating system depending on available memory resources. Data contained in the data buffer is used to update the original model data distributions in each of the K clusters over all M models. Some of the data belonging to a cluster is summarized or compressed and stored as a reduced form of the data representing sufficient statistics of the data. More data is accessed from the database and the models are updated. An updated set of parameters for the clusters is determined from the summarized data (sufficient statistics) and the newly acquired data. Stopping criteria are evaluated to determine if further data should be accessed from the database.Other References
Field of SearchQuery processing (i.e., searching)Query formulation, input preparation, or translation Query augmenting and refining (e.g., inexact access) Pattern matching access Sorting Distributed or remote access Manipulating data structure (e.g., compression, compaction, compilation) Generating database or data structure (e.g., via user interface) MACHINE LEARNING KNOWLEDGE PROCESSING SYSTEM Frame-based reasoning system Trainable classifiers or pattern recognizers (e.g., adaline, perceptron) Sequential decision process (e.g., decision tree structure) Classification Cluster analysis With a multilevel classifier Statistical decision process Weight Quantitative determination by weight Article count or size distribution Weight Electronic shopping (e.g., remote ordering) Presentation of image or description of sales item (e.g., electronic catalog browsing) AUTOMATED ELECTRICAL FINANCIAL OR BUSINESS PRACTICE OR MANAGEMENT ARRANGEMENT | |
