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The concept of association rules was popularized particularly due to the 1993 article of Agrawal et al., [2] which has acquired more than 23,790 citations according to Google Scholar, as of April 2021, and is thus one of the most cited papers in the Data Mining field.
In data mining and association rule learning, lift is a measure of the performance of a targeting model (association rule) at predicting or classifying cases as having an enhanced response (with respect to the population as a whole), measured against a random choice targeting model.
In fact, affinity analysis takes advantages of studying attributes that go together which helps uncover the hidden patterns in a big data through generating association rules. Association rules mining procedure is two-fold: first, it finds all frequent attributes in a data set and, then generates association rules satisfying some predefined ...
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 actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining).
An associative classifier (AC) is a kind of supervised learning model that uses association rules to assign a target value. The term associative classification was coined by Bing Liu et al., [1] in which the authors defined a model made of rules "whose right-hand side are restricted to the classification class attribute".
Also, he teaches the course "Data Mining" during the Fall and Spring semesters at UIC. The course usually involves a project and various quiz/examinations as grading criteria. He is best known for his research on sentiment analysis (also called opinion mining), fake/deceptive opinion detection, and using association rules for prediction.
A common practice in data mining is to classify, to look at the attributes of an object or situation and make a guess at what category the observed item belongs to.As new evidence is examined (typically by feeding a training set to a learning algorithm), these guesses are refined and improved.