Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Treatment of Missing Values for Association Rules
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Association Rules in Incomplete Databases
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Missing Values: Proposition of a Typology and Characterization with an Association Rule-Based Model
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Mining interesting sets and rules in relational databases
Proceedings of the 2010 ACM Symposium on Applied Computing
Mining uncertain data for frequent itemsets that satisfy aggregate constraints
Proceedings of the 2010 ACM Symposium on Applied Computing
Discovery of characteristic patterns from tabular structured data including missing values
International Journal of Business Intelligence and Data Mining
Frequent itemset mining of uncertain data streams using the damped window model
Proceedings of the 2011 ACM Symposium on Applied Computing
Discovery of characteristic patterns from transactions with their classes
Applied Computational Intelligence and Soft Computing
Optimum estimation of missing values in randomized complete block design by genetic algorithm
Knowledge-Based Systems
| Hi-index | 0.00 |
Missing values make up an important and unavoidable problem in data management and analysis. In the context of association rule and frequent itemset mining, however, this issue never received much attention. Nevertheless, the well known measures of support and confidence are misleading when missing values occur in the data, and more suitable definitions typically don't have the crucial monotonicity property of support. In this paper, we overcome this problem and provide an efficient algorithm, XMiner, for mining association rules and frequent itemsets in databases with missing values. XMiner is empirically evaluated, showing a clear gain over a straightforward baseline-algorithm.