Memory-based frequent pattern mining method

Abstract

The invention discloses a memory-based frequent pattern mining method. The method comprises the following steps of 1, creating an initial frequent pattern tree, creating a root node T of the frequent pattern tree, and marking the root node with ''null''; scanning a database again, selecting out frequent items in each read transaction, and sorting the frequent items according to an order in L; creating a path of the frequent pattern tree by taking null as the root node after sorting, adding 1 to a count of only a final node on the path, and keeping counts of other nodes on the path unchanged; scanning all transactions in the whole database in sequence and obtaining the initial frequent pattern tree; and 2, traversing the initial frequent pattern tree in sequence by using a depth-first search algorithm, wherein a counter value of a traversed node is the sum of the value of the node and the values of all child nodes of the node. The method has the technical effects that the write operation of an NVM (Non-Volatile Memory) can be reduced, so that the frequent pattern tree can be quickly established; and a large amount of dense write operations on a node counting domain close to the root node can be reduced, so that the service life of the NVM is prolonged.

Description

technical field [0001] The invention belongs to the technical field of memory, and in particular relates to a memory-based frequent pattern mining method. Background technique [0002] With the increasing maturity of computer technology, data analysis has developed greatly since its establishment in the 20th century. Data analysis can discover and extract items of interest from massive amounts of data, thereby providing guidance to decision-making agencies. Machine learning and data mining can reveal the hidden information behind the data, which has become the key technology of data analysis. [0003] In the field of data mining, finding frequent items or frequent patterns in data sets is an important topic in data mining research, and it is the basis of many important data mining tasks such as correlation analysis, sequential patterns, causal relationships, and revealing patterns. Currently there are techniques such as Apriori and FP-tree to deal with frequent pattern min...

AI Technical Summary

Problems solved by technology

However, using NVM as the main memory has the following problems: First, the time difference between reading and writing operations on NVM is relatively large, and reading operations usually consume more time and energy than writing operations; second, the number of NVM writing operations is limited and uneven The write operation usually accelerates the failure of the whole block NVM

[0011] However, there are problems with the FP-tree algorithm: in the process of constructing the frequent pattern tree, every time an item in a transaction is scanned, the FP-tree must be updated, that is, the nodes of the corresponding item in the FP-tree are counted The domain performs write operations, which leads to a large number of repeated write operations, and the memory overhead is huge; and the closer to the root node, the more write operations, and intensive large-scale write operations will reduce the service life of NVM

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