Frequent Itemset Mining (FIM) is one of
the most well known techniques to extract knowledge from data
. The combinatorial explosion of FIM methods become even more problematic when they are applied to Big Data.
What is frequent itemset in data mining?
Definition. Frequent itemsets (Agrawal et al., 1993, 1996) are
a form of frequent pattern
. Given examples that are sets of items and a minimum frequency, any set of items that occurs at least in the minimum number of examples is a frequent itemset.
What is frequent itemset in big data analytics?
Itemset mining is a well-known exploratory data mining technique used
to discover interesting correlations hidden in a data collection
. Since it supports different targeted analyses, it is profitably exploited in a wide range of different domains, ranging from network traffic data to medical records.
What is frequent itemset and frequent subsequence?
Frequent patterns are itemsets, subsequences,
or substructures that appear in a data set with frequency no less than a user-specified threshold
. For example, a set of items, such as milk and bread, that appear frequently together in a transaction data set, is a frequent itemset.
What is frequent itemset generation?
A frequent itemset is
an itemset whose support is greater than some user-specified minimum support
(denoted L
k
, where k is the size of the itemset) A candidate itemset is a potentially frequent itemset (denoted C
k
, where k is the size of the itemset)
How many phases are there in FP growth algorithm?
The FP-growth algorithm has ________ phases.
four
.
Is an essential process where intelligent methods are applied to extract data pattern?
Data mining
It is an essential process where intelligent methods are applied to extract data patterns. Methods can be summarization, classification, regression, association, or clustering.
What is the purpose of frequent itemset mining?
Frequent Itemset Mining is a
method for market basket analysis
. It aims at finding regularities in the shopping behavior of customers of supermarkets, mail-order companies, on-line shops etc. ⬈ More specifically: Find sets of products that are frequently bought together.
Which is the direct application of frequent itemset mining?
The frequent itemsets mining, a type of association rule mining, was developed in 1990s to
analyze which groups of goods or sets of items were frequently purchased together
. It has been used extensively in commercial marketing [10]–[12].
What is a closed frequent itemset?
Definition: It is a frequent itemset that
is both closed and its support is greater than or equal to minsup
. An itemset is closed in a data set if there exists no superset that has the same support count as this original itemset.
What is a frequent pattern growth?
Frequent Pattern Growth Algorithm is
the method of finding frequent patterns without candidate generation
. It constructs an FP Tree rather than using the generate and test strategy of Apriori. The focus of the FP Growth algorithm is on fragmenting the paths of the items and mining frequent patterns.
What is frequent itemset in Apriori algorithm?
Apriori algorithm uses frequent itemsets to generate association rules. It is based on the concept that a subset of a frequent itemset must also be a frequent itemset. Frequent Itemset is
an itemset whose support value is greater than a threshold value(support)
.
How many frequent Itemsets are there?
Thus, there are only
ten possible frequent doubletons
. Fig. 6.2 is a table indicating which baskets contain which doubletons. Each appears at least three times; for instance, 1dog, catl appears five times.
How do I generate frequent Itemsets?
- Reduce the number of candidates: use pruning techniques such as the Apriori principle to eliminate some of the candidate itemsets without counting their support values.
- Reduce the number of transactions: by combining transactions together we can reduce the total number of transactions.
What is maximal frequent itemset and closed frequent itemset?
Then what are closed and maximal frequent itemsets? By definition,
An itemset is maximal frequent if none of its immediate supersets is frequent
. An itemset is closed if none of its immediate supersets has the same support as the itemset .
What is Apriori principle?
Put simply, the apriori principle states that.
if an itemset is infrequent, then all its supersets must also be infrequent
. This means that if {beer} was found to be infrequent, we can expect {beer, pizza} to be equally or even more infrequent.
