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ACT-Apriori algorithm based on self-encoding technology

A self-coding and algorithmic technology, applied in computing, database models, instruments, etc., can solve the problems of increasing the computing burden and combinatorial explosion of the global database, and achieve the effect of solving the difficulty of establishing and updating, reducing the dimension of data and the degree of reduction.

Pending Publication Date: 2022-07-26
CHINA THREE GORGES UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to provide an association rule algorithm based on self-encoding technology—ACT-Apriori, which solves the problem that the traditional Apriori algorithm and FP-Tree algorithm need to scan the global database twice to increase the calculation burden. When the database is large, it is easy Problems with Combinatorial Explosions

Method used

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  • ACT-Apriori algorithm based on self-encoding technology
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  • ACT-Apriori algorithm based on self-encoding technology

Examples

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example 1

[0065] If the given transaction database is shown in Table 1,

[0066] Table 1 The original transaction database

[0067] TID Items 1 A,C,E,F,H 2 A,B,E,F,G 3 A,B,C,D,F,G 4 A,B,C,F,G 5 A,B,C,E 6 A,B,F,G 7 A,C,E,F 8 A,B,C,F,G 9 A,C,D 10 C,B,G

[0068] There are 10 transactions in the transaction database. First, the support count of each item in the entire transaction database is calculated as shown in Table 2.

[0069] Table 2 Support count of each item

[0070]

[0071] Taking NS=2, then A and C are items with higher support thresholds (ie high-frequency items), then the item set list TF={A,C}. By definition, the itemset list TI is all possible permutations in TF, so we can create 2 from TF 2 itemsets, so TI={{}, {A}, {C}, {A, C}}. Second, delete the item contained in TF from the original transaction database, and replace its position in the original database with a self-encoding bit vector to ...

example 2

[0086] This example shows the operation of the frequent itemset generation stage by using the RDB, TF, and TI represented in Example 1. In this example, the support threshold is set to 50%. In the first pass, the ACT-Apriori algorithm creates candidate item sets c[1] = {B}, {D}, {E}, {F}, {G}, {H}. Then, with the transaction bits (SNT) computed from the data preprocessing stage, the algorithm computes the support for each candidate itemset and its combination with the TI list. For example, for the candidate item set {B}, compute the support counts for {B}, {B, A}, {B, C} and {B, A, C}, where TI = {{}, {A}, { C}, {A, C}}. Tables a1 to a6 show the results when k=1. All itemsets with support greater than the minimum support threshold will be retained. So the set of items kept at the end of pass 1 are: {B}, {B, A}, {B, C}, {F}, {F, A}, {F, C}, {F, A , C}, {G}, {G, A}.

[0087] Table a1 Candidate Frequent 1 Itemset Generation Table (c[1]={B})

[0088]

[0089] Table a2 ca...

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Abstract

An ACT-Apriori algorithm based on a self-encoding technology comprises the following steps: firstly, preprocessing a transactional database D, neglecting a high-frequency parameter NS of each item in the transactional database D, and replacing the high-frequency parameter NS with a self-encoding bit vector SNT to form a simplified database RBD, so that the data dimension is greatly reduced; according to the method, data records are all read into a memory after being simplified, in the process of generating candidate item sets through frequent item set connection and pruning, the process of generating the candidate item sets is improved, the candidate item sets are directly generated, the database is scanned after the candidate item sets are obtained, and the support degree is calculated; when a candidate item set is searched in each record, the search of the transaction can be stopped once a value greater than a candidate item is searched. Compared with an existing brand-new association rule algorithm, the algorithm has the advantages that the calculation time is greatly shortened, the memory occupancy is greatly reduced, and the time complexity and the space complexity are obviously optimized.

Description

technical field [0001] The invention belongs to the technical field of big data mining algorithms, in particular to an ACT-Apriori algorithm based on self-encoding technology. Background technique [0002] Smart grid is a new type of power grid that uses modern network information technology to realize the exchange of data and information between power grid equipment, so as to realize real-time automatic control, intelligent adjustment, online decision analysis and other functions of the power grid. The construction of smart grid has accumulated massive data resources. At present, power companies are managing enterprises based on data, and the demand for information-driven business is becoming more and more urgent. The smart grid big data has the characteristics of "4V", namely: large volume (Volume), variety (Variety), high speed (Velocity), and low value density (Value). In recent years, with the improvement of power informatization, data such as power equipment condition...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/2453G06F16/2458G06F16/22G06F16/28
CPCG06F16/24549G06F16/2465G06F16/2246G06F16/28
Inventor 程江洲闫冉阳张晓瑀冯梦婷冯馨以
Owner CHINA THREE GORGES UNIV
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