Efficient incremental method for data mining of a database

• US 20030217055A1
• Filed: 05/20/2002
• Published: 11/20/2003
• Est. Priority Date: 05/20/2002
• Status: Abandoned Application

• Current Assignee: Chang-Huang Lee, Ming-Syan Chen
• Original Assignee: Chang-Huang Lee, Ming-Syan Chen
• Inventors: Lee, Chang-Huang, Chen, Ming-Syan
• Application Number:
  US10/153,017
• Publication Number:
  US 20030217055A1
• Time in Patent Office: Days
• US Class Current: 707/6
• CPC Class Codes: G06F 16/2465 Query processing support for facilitating data mining operations in structured databases

Abstract

A method for discovering association rules in an electronic database commonly known as data mining. A database is divided into a plurality of sections, and each section is sequentially scanned, the results of the previous scan being taken into consideration in a current scanned partition. Three algorithms are further developed on this basis that deal with incremental mining, mining general temporal association rules, and weighted association rules in a time-variant database.

Claims

1. A pre-processing method for data mining, comprising:

dividing a database into a plurality of partitions;

scanning a first partition for generating a plurality of candidate itemsets;

developing a filtering threshold based on each partition and removing the undesired candidate itemsets; and

scanning a second partition while taking into consideration the desired candidate itemsets from the first partition.

2. The method of claim 1, wherein the generation of candidate itemsets includes the steps of:

assigning a candidate itemset a value of when an itemset was added to an accumulator; and

adding a value for the number of occurrences of the itemset from the point the itemset to the accumulator.

3. The method of claim 1, wherein the step of removing the undesired candidate itemsets is based on a minimum threshold requirement as defined by the filtering threshold.

4. A method for mining general temporal association rules, comprising:

dividing a database into a plurality of partitions including a first partition and a second partition;

scanning the first partition for generating candidate itemsets;

developing a filtering threshold based on the scanned first partition and removing the undesired candidate itemsets;

scanning the second partition while taking into consideration the desired candidate itemsets from the first partition;

performing a scan reduction process by considering an exhibition period of each candidate itemset;

scanning the database to determine the support of each of the candidate itemsets in the filtering threshold; and

pruning out redundant candidate itemsets that are not frequent in the database and outputting the final itemsets.

5. The method of claim 4, wherein the generation of candidate itemsets includes the step of assigning a candidate itemset a value of when an itemset was added to an accumulator and adding a value for the number of occurrences of the itemset from the point the itemset to the accumulator.

6. The method of claim 4, wherein the removal of undesired candidate itemsets is based on a minimum threshold requirement as defined by the filtering threshold.

8. The method of claim 6, wherein the generation of the candidate itemsets includes the step of assigning a candidate itemset a value of when an itemset was added to an accumulator, and adding a value for the number of occurrences of the itemset from the point the itemset to the accumulator.

9. The method of claim 6, wherein the removal of the undesired candidate itemsets is based on a minimum threshold requirement as defined by the filtering threshold.

7. A method for incremental mining comprising:

dividing a database into a plurality of partitions, including a first partition and a second partition;

scanning the first partition for generating a plurality of candidate itemsets;

developing a filtering threshold based on each of the partitions and removing undesired candidate itemsets of the candidate itemsets;

removing transactions from the candidate itemset based on a previous partition; and

adding transactions to the itemset based on a next partition.