Fequent Itemset Mining Case Study
Chapter 03
Recent advancements in technology provide an opportunity to construct and store the huge amount of data together from many fields such as business, administration, banking, the delivery of social and health services, environmental safety, security and in politics. Typically, these data sets are very huge and regularly growing and contain a huge number of compound features which are hard to manage. Therefore, mining or extracting association rules from large amount of data in the database is interested for many industries which can help in many business decision making processes, such as cross-marketing, basket data study, and promotion assortment. From the beginning, Frequent Itemset Mining (FIM) is one of the most well known techniques which is concerned with extracting the information from databases based on regularly …show more content…
1. Apriori faces complexity in mining long pattern, particularly for dense datasets. For example, to discover a frequent itemsets of X = {1…200} items. Apriori has to generate-and-test all 2200 candidates.
2. Apriori algorithm is considered to be an improper for handling frequency counting, which is the most exclusive task in frequent itemsets mining. Since Apriori is a level-wise candidate-generate-and-test algorithm, therefore it has to scan the dataset 200 times to find a frequent itemsets X = X1… X200.
3. Even though Apriori algorithm reduces the size of the search space by removing all k itemsets, which are uncommon before generating candidate frequent (k+1)-itemsets, it still requires scanning of the dataset in sort to determine which candidate (k+1) itemsets are frequent and which are infrequent. Even for datasets which have 200 items, determining k-frequent itemsets by repeated scanning the dataset with pattern matching takes a huge amount of processing
Recent advancements in technology provide an opportunity to construct and store the huge amount of data together from many fields such as business, administration, banking, the delivery of social and health services, environmental safety, security and in politics. Typically, these data sets are very huge and regularly growing and contain a huge number of compound features which are hard to manage. Therefore, mining or extracting association rules from large amount of data in the database is interested for many industries which can help in many business decision making processes, such as cross-marketing, basket data study, and promotion assortment. From the beginning, Frequent Itemset Mining (FIM) is one of the most well known techniques which is concerned with extracting the information from databases based on regularly …show more content…
1. Apriori faces complexity in mining long pattern, particularly for dense datasets. For example, to discover a frequent itemsets of X = {1…200} items. Apriori has to generate-and-test all 2200 candidates.
2. Apriori algorithm is considered to be an improper for handling frequency counting, which is the most exclusive task in frequent itemsets mining. Since Apriori is a level-wise candidate-generate-and-test algorithm, therefore it has to scan the dataset 200 times to find a frequent itemsets X = X1… X200.
3. Even though Apriori algorithm reduces the size of the search space by removing all k itemsets, which are uncommon before generating candidate frequent (k+1)-itemsets, it still requires scanning of the dataset in sort to determine which candidate (k+1) itemsets are frequent and which are infrequent. Even for datasets which have 200 items, determining k-frequent itemsets by repeated scanning the dataset with pattern matching takes a huge amount of processing