Improved Apriori algorithm, and application of the same in Tibetan-medicine association mining

An algorithm and Tibetan medicine technology, applied in the application field of Tibetan medicine association mining, can solve the problems of lack of standardization of Tibetan medicine terminology, low level and level of clinical research, and low standardization of Tibetan medicine diagnosis and treatment technology, so as to avoid medical errors and reduce time. , the effect of improving operating efficiency

Pending Publication Date: 2019-06-07
QINGHAI UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, due to the relatively late development of the modernization of Tibetan medicine, there are still many new problems and difficulties i

Method used

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  • Improved Apriori algorithm, and application of the same in Tibetan-medicine association mining
  • Improved Apriori algorithm, and application of the same in Tibetan-medicine association mining
  • Improved Apriori algorithm, and application of the same in Tibetan-medicine association mining

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0023] Embodiment 1 Comparison between Apriori algorithm and FP-Growth algorithm

[0024] (1) Experimental purpose: To investigate the efficiency and effect of Apriori algorithm and FP-Growth algorithm.

[0025] (2) Experimental environment:

[0026] Processor: Intel Core i5-2450M CPU2.5GHz

[0027] Memory: 4GB

[0028] Hard disk: 640GB

[0029] OS: Windows 7

[0030] Development environment: Myeclipse

[0031] (3) Experimental data: the data set comes from the UCI machine learning database, because whether the attribute value is discrete directly affects the process of association rule extraction, so the present invention selects itself discrete or only needs a small amount of discrete test data set to test the algorithm efficiency.

[0032] (4) Experimental content

[0033] The Apriori algorithm and the FP-Growth algorithm are compared and tested. On different data sets, by changing the minimum support threshold, the time required for the two algorithms to find freque...

Embodiment 2

[0047] Embodiment 2Apriori algorithm compares with improved Apriori algorithm

[0048] (1) Experimental purpose: To investigate the efficiency and effect of Apriori algorithm and improved Apriori algorithm.

[0049] (2) Experimental environment:

[0050] Processor: Intel Core i5-2450M CPU 2.5GHz

[0051] Memory: 4GB

[0052] Hard disk: 640GB

[0053] OS: Windows 7

[0054] Development environment: Myeclipse

[0055] (3) Experimental data: The data set comes from the UCI machine learning database, which has 53 attributes and 18 records.

[0056] (4) Experimental content

[0057] The Apriori algorithm and the improved Apriori algorithm are compared and tested. On the same data set, by changing the minimum support threshold, the time required for the two algorithms to find frequent itemsets is tested, and the average value is calculated by running 10 times under the same environment.

[0058] (5) Experimental results

[0059] Table 4 Comparison table of running time betwe...

Embodiment 3

[0063] Example 3 Apriori Algorithm Applied to Diagnosis and Treatment of Tibetan Medicine

[0064] The Apriori algorithm in the association rules generates candidate item sets by scanning the data set layer by layer, and then screens out frequent item sets that meet the requirements from a large number of candidate item sets according to the support degree, and generates association rules. The selection of the support degree greatly affects the number of frequent itemsets mined and the usefulness of the generated association rules. The present invention mines 21 data records belonging to hot type brucellosis and 24 data records belonging to cold type brucellosis respectively, generates association rules between symptoms and symptom types, and frequently excavates under different support degrees. The number of itemsets such as figure 1 shown.

[0065] It can be seen that selecting the appropriate support degree plays a decisive role in finding useful rules. According to the s...

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Abstract

The invention discloses an improved Apriori algorithm, and an application of the same in Tibetan-medicine association mining, capable of converting a transaction database into a Boolean matrix only byscanning the transaction database for one time, and then converting scanning of the transaction database into vector operation. In the matrix, rows represent transactions, columns represent data items, and if one item is appears in a transaction, the item is represented by 1, and the item is represented by 0 if the item does not appears. The Boolean matrix is more compact than the transaction database as the Boolean matrix does not need to scan the data set repeatedly, and the support degree is calculated by vector operation. The improved vector-based Apriori algorithm is significantly betterthan the original Apriori algorithm. The improved Apriori algorithm is applied to the field of Tibetan medicine diagnosis and treatment to assist medical decision-making analysis, can help Tibetan medical workers to obtain useful information in a timely and accurate manner, can effectively avoid medical errors, can improve the modernization level of Tibetan medicine, and can provide a powerful tool for assisting Tibetan medicine diagnosis and treatment by using the advanced science and technology.

Description

technical field [0001] The invention relates to an improved Apriori algorithm and its application, specifically, an improved Apriori algorithm capable of accelerating the discovery of frequent itemsets and its application in Tibetan medical association mining are designed. Background technique [0002] The Apriori algorithm uses the method of finding frequent itemsets in the candidate item set to achieve better performance, but there is still a problem of low efficiency. The running time is mainly consumed in three aspects: to generate each level of frequent patterns, it is necessary to repeat Scanning the database, the I / O load is heavy; when the data set is large, the number of candidate item sets generated increases exponentially, and the amount of calculation is huge; it takes a lot of time to match a large number of candidate item sets with transactions, and cannot be widely obtained. Application, especially in the process of Tibetan medicine syndrome classification. ...

Claims

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Application Information

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IPC IPC(8): G16H50/70G16H50/20
Inventor 王璐张磊祝小兰王世颍王雪茜刘超逸张拂晓
Owner QINGHAI UNIVERSITY
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