Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting change in categorical data: mining contrast sets
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Classifying text documents by associating terms with text categories
ADC '02 Proceedings of the 13th Australasian database conference - Volume 5
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Frequent term-based text clustering
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
COFI approach for mining frequent itemsets revisited
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
| Hi-index | 0.00 |
In many languages, the English word “computer” is often literally translated to “the counting machine.” Counting is apparently the most elementary operation that a computer can do, and thus it should be trivial to a computer to count. This, however, is a misconception. The apparently simple operation of enumeration and counting is actually computationally hard. It is also one of the most important elementary operation for many data mining tasks. We show how capital counting is for a variety of data mining applications and how this complex task can be achieved with acceptable efficiency.