Mining quantitative association rules in large relational tables

By Rayadurgam Srikant, Rakesh Agrawal. 1996

We introduce the problem of mining association rules in large relational tables containing both quantitative and categorical attributes. An example of such an association might be "10% of married people between age 50 and 60 have at least 2 cars". We deal with quantitative attributes by fine-partitioning the values of the attribute and then combining adjacent partitions as necessary. We introduce measures of partial completeness which quantify the information lost due to partitioning. A direct application of this technique can generate too many similar rules. We tackle this problem by using a "greater-than-expected-value" interest measure to identify the interesting rules in the output. We give an algorithm for mining such quantitative association rules. Finally, we describe the results of using this approach on a real-life dataset.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model (2015, neutral): “For data with continuous features, a variety of procedures exist for antecedent mining [Fayyad and Irani (1993), Dougherty, Kohavi and Sahami (1995), Srikant and Agrawal (1996)].”
A Tree Projection Algorithm for Generation of Frequent Item Sets (2001, background): “Variations of association rules such as generalized association rules, quantitative association rules and multilevel association rules have been studied in [9, 14, 15].”
Approximate and Exact Enumeration of Rule Models (2018, methodology): “The algorithms described in this paper can also be applied to real-valued data by using discretization techniques (Srikant and Agrawal 1996; Fukuda et al. 1996; Rastogi and Shim 2002).”
Personalized privacy preservation (2006, background): “scientists), who may explicitly specify the analytical tasks (such as association rule mining [14]) required.”
A framework for high-accuracy privacy-preserving mining (2004, background): “ demonstrate in this section how it can be used for enhancing privacy-preservingmining ofassociation rules, a popular mining model that identiﬁes interesting correlations b etween database attributes [1, 21]. The core of the association rule mining is to identify “frequent itemsets”, that is, all th”
Efficient k -Anonymization Using Clustering Techniques (2007, background): “in [3] propose an algorithm which randomizes data to prevent association rule mining [28].”
Frequent item set mining (2012, methodology): “Extensions of standard association rule induction include, among many others, the incorporation of taxonomies for the items, 59 quantitative association rules, 60 and fuzzy association rules, 61 which use fuzzy sets over continuous domains as items.”
Maintaining Data Privacy in Association Rule Mining (2002, unknown): “In our future work, we plan to investigate the extension of our results to generalized [17] and quantitative [18] association rules.”
Association mining (2006, methodology): “Other techniques postanalysis techniques of significance have also been proposed [Srikant and Agrawal 1996; Liu et al. 1999].”
Pruning and summarizing the discovered associations (1999, methodology): “Those rules that do not meet this minimum improvement i n confidence a re pruned. [ 24 ] also p roposed a related technique.”
Detecting Group Differences: Mining Contrast Sets (2001, methodology): “During the counting phase we organize nodes into candidate groups Bayardo, 1998; Srikant & Agrawal, 1996.”
Automatic Subspace Clustering of High Dimensional Data (2005, background): “The subspace identification problem is related to the problem of finding quantitative asso - ciation rules that also identify interesting regions of various attributes ( Srikant and Agrawal , 1996 ; Miller and Yang , 1997 ) .”
Algorithms for association rule mining — a general survey and comparison (2000, unknown): “[24], generalized association rules, e.”
CORDS: automatic discovery of correlations and soft functional dependencies (2004, background): “Another related body of work concerns the mining of association rules and semantic integrity constraints; see, for example, [18, 19].”
Data Mining Techniques (2001, methodology): “Several algorithms have been discussed in Srikant and Agrawal (1996b), Agrawal and Srikant (1995), Oates, Schmill, Jensen and Cohen (1997) and Zaki (1998, 2000).”
Mining Access Patterns Efficiently from Web Logs (2000, unknown): “Srikant and Agrawal 122 generalized their deenition of sequential patterns in 22 to include time constraints, sliding time window, and user-deened taxonomy and developed a generalized sequential pattern mining algorithm, GSP, which outperforms their AprioriAll algorithm 22.”
Smart Drill-Down: A New Data Exploration Operator (2014, methodology): “Several extensions have been proposed to the a-priori algorithm, including those for dealing with numerical attributes [23, 19].”