# Apriori algorithm in the improvement and implementation of e-commerce based on data mining

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4 mining association in order to find the data implied by law and data between; d) data modelling and parameter adjustment; e) data-based application development and decision support. The following examples to illustrate. 5.1 APRIORI ALGORITHM The Apriori algorithm used in the system will be introduced in detail as follows. The pseudo-code in the Apriori algorithm is as follows: Input : Ts DataBase D Input : Minimum support threshold min _ sup Output : Frequent pattern L { L1 Large 1 itemsets ; for 2; L! NULL; K 1 1 C ' join L L ; C ' pruning C ' ; C ' A gen L ; { m n for ts t D C minset( C, t); t for all candidates c C c. count; L c C c. count min _ sup } for all minset s L { ifconf ( s L s) min conf ; printf (" s L s") ; }} t FIGURE 1 The pseudo-code in the Apriori algorithm 5.2 APRIORI ALGORITHM IMPROVEMENT t main memory space. Therefore, the candidate item sets should properly distribute the internal memory. The technology and the method of realizing the fast scanning to the large-scale database system is to improve the efficiency of the management rules. It is very important to effectively extract the association rules from the massive data facing up with the mass of the database. According to the operations of the above examples, the rule can be obtained. If there is no N frequent item sets in each database, the N+1 frequent item sets cannot be included. In the meantime, the support level in the N item sets is irrelevant to the item sets which are smaller than the N item sets, it is unnecessary to consider it. It is unnecessary to scan the segments which are smaller than the N item sets. The processing number of the data can be reduced. The auxiliary table F should be set up, and the thing number and the segment length in the table F should be recorded. In addition, the auxiliary table should be updated according to its orders and the item sets which is smaller than the segment length and the item sets which are not included should be deleted. The improved Apriori algorithm should scan the auxiliary table during the operation, and the record which does not exist in the auxiliary cannot be scanned. If the produced Ln frequent N item set is larger than its segment length, the record not included in the frequent item sets cannot exist in the auxiliary table, and the auxiliary table should be updated. The auxiliary table should be scanned at first and then the records not included in the auxiliary table can be sipped so that its efficiency can be improved. 5.3 THE REALIZATION OF THE IMPROVED APRIORI ALGORITHM The biggest problem in the Apriori algorithm is that the source database should be repeatedly scanned once. The algorithm not only produces a mass of candidate item sets, but also enlarging the load capacity of the system, delaying the handling time and consuming a large amount of the TABLE 1 The things database The following example illustrates the application of the improved Apriori algorithm. The things database is as shown in the Table 1 and the minimum support level is assumed as 4. Things number Item set Things number Item set Things number Item set T 1 A,B,C,D,E T 4 A,B,C,E T 7 A,B T T 5 E T T 3 A,C,E T 6 C,E T 9 A,B,C,D,E 1) Scan the database. The appearance of each item should be counted and then the C i candidate item sets can be formed, as shown in the Table 2. The internal memory should be put in the data table so that the initial auxiliary table F1 can be produced, as shown in the Table 3. TABLE 2 C1 candidate item sets {A} 5 {B} 4 {C} 5 {D} 2 {E} 6 TABLE 3 F1 auxiliary table Things number Length of the field Things number T1 5 T6 2 T2 0 T7 2 T3 3 T8 0 T4 4 T9 5 T Length of the field 157

5 2) Delete the item sets whose C i candidate item sets are smaller than the defined support level 4, as shown in the Table 4. The F1 auxiliary table should be updated and the records whose segment length is smaller than 1 and the records which are not included in the L1 frequent 1- item sets should be deleted so that the F2 auxiliary table can be obtained, as shown in the Table 4. TABLE 4 L1 frequent 1- item sets {A} 5 {B} 4 {C} 5 {D} - {E} 6 3) Connect the L1 frequent 1- item sets and the C2 candidate item sets can be produced, as shown in the Table 5. Through scanning the data table and the F2 auxiliary table, the L2 frequent 2- item sets can be obtained by deleting the item sets, which cannot meet the requirements of the minimum support level. As shown in the Table 6. The F3 auxiliary table can be obtained by deleting those records whose segment length is less than 2 and which are not included in the L2 frequent 2- item sets, as shown in the Table 7. TABLE 5 TABLE 6 TABLE 7 C2 candidate item sets itemsets {A,B} 4 {A,C} 4 {A,E} 4 {B,C} 3 {B,E} 3 {C,E} 5 L2 frequent 2 - item sets itemsets {A,B} 4 {A,C} 4 {A,E} 4 {C,E} 5 F3 auxiliary table Things number length of the field T 1 5 T 3 3 T 4 4 T 9 5 4) Connect the L2 frequent 2- item sets and the C3 candidate item sets can be produced, as shown in the Table 8. Through scanning the data table and the F3 auxiliary table, the L3 frequent 3- item sets can be obtained by deleting the item sets which cannot meet the requireements of the minimum support level, as shown in the Table 9. The algorithm operation should be concluded and all frequent item sets should be obtained, as shown in the Table 10. TABLE 8 TABLE 9 C3 candidate item sets {A,B,C} 3 {A,B,E} 3 {A,C,E} 4 {A,B,C,E} 5 L3 frequent 3 item sets {A,C,E} 4 TABLE 10 Frequent item sets {A,B} 4 {A,C} 4 {A,E} 4 {C,E} 5 {A,C,E} 4 The operation can be accelerated and its efficiency can be obviously increased by improving the algorithm for the data in the data table is directly put in the internal memory by the system. The speed of inquiry can be accelerated for it is unnecessary to scan the previous database table again in each scanning and it just needs to visit the internal memory directly. The join of the auxiliary table can also acelerate the improvement of the efficiency. The auxiliary table can reduce the visits which is irrelevant to the records so that the number of the records in the data table can also be obviously reduced and the time can also be reduced. 5.4 THE COMPARATIVE ANALYSIS BETWEEN THE BEFORE ALGORITHM AND THE AFTER ALGORITHM In order to verify the improved algorithm, the same software is installed in a host computer. The before improved Apriori algorithm and the after improved Apriori algorithm should be tested in the same support level so that the time comparison can be finished and the improving effect can be judged. If the nine item sets are assumed as (T1, T2, T3, T4, T5, T6, T7, T8, T9) and there are 4000 data, the operating time should be calculated in the same time complexity, as shown in the Table 11. TABLE 11 The operating time table between the before algorithm and the after algorithm in the same support level Before improved Apriori algorithm After improved Apriori algorithm 6 Conclusion 49s 36s 23s 14s 12s 9s 7s 33s 24s 14s 11s 9s 7s 6s When the support level is larger and there are less algorithm operating times, the auxiliary table and the application efficiency of extracting the algorithm is low and the 158

6 advantages of the algorithm are not obvious. When the support level is smaller, the affair, which is not conformed to segment length, should be deleted, the processing time can be reduced and the database table can be reduced so that the advantages of time in the figure are obvious. In summary, the efficiency of the after improved Apriori algorithm can be improved to a certain extent and the processing time is more evident during a smaller support level. When there are a large amount of the data, the corresponding auxiliary F can also be enlarged and the internal memory space can be reduced correspondingly. In the meantime, the processing time of extracting the algorithm can be influenced so that the algorithm needs to be further improved. References [1] HadoopMapReduce [2] HDFS [3] Apache Hadoop NextGen MapReduce (YARN) [4] Manyia J, Chui M, Brown B, et al.2011 Big data; the next frontier for innovation, competition, and productivity [5] [6] Garg S K, Versteeg S, Buyya R 2013 Future Generation Computer Systems 29(4) [7] Nurmi D, Wolsi R, Grzegorczy C, et al 2009 the eucalyptus opensource cloud-computing system Cluster Computing and the Grid 2009 CCGRID'09 9 th IEEE/ACM International Symposium on. IEEE [8] Moreno-Vozmediano R, Montero R S, Llorente I M 2013 Key Challenges in Cloud Computing: Enabling the Future Internet of Services Internet Computing IEEE 17(4) [9] Toet A, Hogervorst M A, Niolov S G, Lewis J J, Dixon T D, Bull D R, Canagarajah C N 2010 Towards Cognitive Image Fusion Information Fusion 11(2) [10] Li G, Yang W, Weng T 2007 A Method of Removing Thin Cloud in Remote Sensing Image Based on the Homomorphic Filter Algorithm Science of Surveying and Mapping 32(3) 47-8 [11] Liu Y, Bai J 2008 Research on the Cloud Removal Method of Remote Sensing Images Geomatics & Spatial Information Technology Author Gan Tao, April 13, 1978, Henan Province, China. Current position, grades: associate professor of Zhouou Normal University. University studies: economic information management. Scientific interest: the small and medium-sized enterprise management. 159

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