DATABASE FUNDAMENTALS
DATA WAREHOUSING AND DATA MINING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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It mines frequent itemsets without candidate generation.
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There are chances that FP trees may not fit in the memory
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FP trees are very expensive to build
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It expands the original database to build FP trees.
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Detailed explanation-1: -FP-growth is an improved version of the Apriori Algorithm which is widely used for frequent pattern mining(AKA Association Rule Mining). It is used as an analytical process that finds frequent patterns or associations from data sets.
Detailed explanation-2: -In simple words, this algorithm works as follows: first it compresses the input database creating an FP-tree instance to represent frequent items. After this first step it divides the compressed database into a set of conditional databases, each one associated with one frequent pattern.
Detailed explanation-3: -Disadvantages of FP-Growth Algorithm FP Tree is more cumbersome and difficult to build than Apriori. It may be expensive. The algorithm may not fit in the shared memory when the database is large.
Detailed explanation-4: -FP growth algorithm represents the database in the form of a tree called a frequent pattern tree or FP tree. This tree structure will maintain the association between the itemsets. The database is fragmented using one frequent item.