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Now after the implementation of the database with the two algorithms, it is much clear that the results are accurate and efficient. Changing size of the database or of the algorithm does not effect the accuracy although the algorithm uses different strategies for the production purpose. “Now as the performance were evaluated by changing the size of the database. Next to make the process more reliable the input threshold has to be changed. For that purpose for SI algorithm the input value was changed from .80 to .70 and from twenty to thirty for apriori algorithm. Now after changing the algorithm were applied to scan the database to produce the largest item list. Thus, the item set produced (large) by both of the algorithms are: T1 72 JT4

Table 8 Now after the implementation of the database with the two algorithms, it is much clear that the results are accurate and efficient. Changing size of the database or of the algorithm does not effect the accuracy although the algorithm uses different strategies for the production purpose. “Now as the performance were evaluated by changing the size of the database. Next to make the process more reliable the input threshold has to be changed. For that purpose for SI algorithm the input value was changed from .80 to .70 and from twenty to thirty for apriori algorithm. Now after changing the algorithm were applied to scan the database to produce the largest item list. Thus, the item set produced (large) by both of the algorithms are: T1 72 JT4

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Other method know is sampling minimize the scanning of database to one in their best case while two in their worst case.'° In this technique a portion of the database brought into the main memory first and then it is used to generate the large itemsets by using well known algorithms like apriori. All these approaches are based on the idea that data is stored in the database in some structure like flat-file or may be TID while the actual data in the database is not in that structure. All these algorithms and approaches work on the idea that first to find the large itemsets. In all these approaches Apriori is seem to be the most efficient algorithms. In this research The focus will be on Apriori algorithm. Here two algorithms were applied Apriori and the algorithm produced by us that is SI algorithm. "'
Other method know is sampling minimize the scanning of database to one in their best case while two in their worst case.'° In this technique a portion of the database brought into the main memory first and then it is used to generate the large itemsets by using well known algorithms like apriori. All these approaches are based on the idea that data is stored in the database in some structure like flat-file or may be TID while the actual data in the database is not in that structure. All these algorithms and approaches work on the idea that first to find the large itemsets. In all these approaches Apriori is seem to be the most efficient algorithms. In this research The focus will be on Apriori algorithm. Here two algorithms were applied Apriori and the algorithm produced by us that is SI algorithm. "'
Variables of the type binary can take only two values true or false either 0 or 1. If it is think that al variables having the same weight then a 2 by 2 contingency table is given Table3: Contingency table for binary variables Logical data analysis was firstly developed in the late eighties. Its aim is to discover hidden knowledge pattern in the database of Boolean type. The aim of the LAD is to analyze the Boolean data but then it is extended for numerical data by the introduction of Binarization. The method of LAD was extended to numerical data by the process of Binarization in which numerical data is transferred to the binary values like zero and ones. 16
Variables of the type binary can take only two values true or false either 0 or 1. If it is think that al variables having the same weight then a 2 by 2 contingency table is given Table3: Contingency table for binary variables Logical data analysis was firstly developed in the late eighties. Its aim is to discover hidden knowledge pattern in the database of Boolean type. The aim of the LAD is to analyze the Boolean data but then it is extended for numerical data by the introduction of Binarization. The method of LAD was extended to numerical data by the process of Binarization in which numerical data is transferred to the binary values like zero and ones. 16
More amount of data were loaded which raise number of records into (2000) these 2000 were given to apriori algorithm. Its produce the same largest set of item as was produced with 1000 records given below.
More amount of data were loaded which raise number of records into (2000) these 2000 were given to apriori algorithm. Its produce the same largest set of item as was produced with 1000 records given below.
Now the SI algorithm was implemented on the same amount of data
Now the SI algorithm was implemented on the same amount of data
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