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Apriori [1] is an algorithm for frequent item set mining and association rule learning over relational databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database.
The name of the algorithm is Apriori because it uses prior knowledge of frequent itemset properties. The control flow diagram for the Apriori algorithm. Overview: Apriori uses a "bottom up" approach, where frequent subsets are extended one item at a time (a step known as candidate generation), and groups of candidates are tested against the ...
Rule 1: A implies 0; Rule 2: B implies 1; because these are simply the most common patterns found in the data. A simple review of the above table should make these rules obvious. The support for Rule 1 is 3/7 because that is the number of items in the dataset in which the antecedent is A and the consequent 0. The support for Rule 2 is 2/7 ...
A survey and taxonomy of the key algorithms for item set mining is presented by Han et al. (2007). [5] The two common techniques that are applied to sequence databases for frequent itemset mining are the influential apriori algorithm and the more-recent FP-growth technique.
Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves 'rules' to store, manipulate or apply. [ 1 ] [ 2 ] [ 3 ] The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that ...
GSP algorithm (Generalized Sequential Pattern algorithm) is an algorithm used for sequence mining. The algorithms for solving sequence mining problems are mostly based on the apriori (level-wise) algorithm. One way to use the level-wise paradigm is to first discover all the frequent items in a level-wise fashion.
RULES-F [11] is an extension of RULES-5 that handles not only continuous attributes but also continuous classes. A new rule space representation scheme was also integrated to produce an extended version called RULES-F+ [9]. RULES-SRI [12] is another scalable RULES algorithm, developed to improve RULES-6 scalability.
Ramakrishnan Srikant is a Google Fellow at Google.. His primary field of research is Data Mining.His 1994 paper, "Fast algorithms for mining association rules", [2] co-authored with Rakesh Agrawal has acquired over 27000 citations as per Google Scholar [3] as of July 2014, and is thus one of the most cited papers in the area of Data Mining.