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
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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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