The subject of this paper is the mining of generalized association rules using pruning techniques. Given a large transaction database and a hierarchical taxonomy tree of the items, we attempt to find the association rules between the items at different levels in the taxonomy tree under the assumption that original frequent itemsets and association rules have already been generated in advance. T...

This paper proposes an algorithm to discover novel association rules, combined association rules. Compared with conventional association rule, this combined association rule allows users to perform actions directly. Combined association rules are always organized as rule sets, each of which is composed of a number of single combined association rules. These single rules consist of non-actionabl...

-Generating association rules is generally done by association rule mining algorithm like classical algorithm Apriori, FP-Tree Partition and so on. Genetic algorithm can also implement to generate association rules. Main advantage of Genetic algorithm is that it can perform global search. This paper presents proposed algorithm Multi-Fitness function Genetic algorithm (MFGA) based association ru...

In data mining, the association rules are used to find for the associations between the different items of the transactions database. As the data collected and stored, rules of value can be found through association rules, which can be applied to help managers execute marketing strategies and establish sound market frameworks. This paper aims to use Fuzzy Frequent Pattern growth (FFP-growth) to...

Since huge amounts of spatial data can be easily collected from various applications, ranging from remote sensing technology to geographical information system, the extraction and comprehension of spatial knowledge is a more and more important task. Many excellent studies on Remote Sensed Image (RSI) have been conducted for potential relationships of crop yield. However, most of them suffer fro...

Mining generalized association rules among items in the presence of taxonomies has been recognized as an important model in data mining. Earlier work on mining generalized association rules ignore the fact that the taxonomies of items cannot be kept static while new transactions are continuously added into the original database. How to effectively update the discovered generalized association r...

The major drawback of fuzzy data mining is that after applying fuzzy data mining on the quantitative data, the number of extracted fuzzy association rules is very huge. When many association rules are obtained, the usefulness of them will be reduced. In this paper, we introduce an approach to reduce and summarize the extracted fuzzy association rules after fuzzy data mining. In our approach, in...

Association Rule play very important role in recent scenario of data mining. But we have only generated positive rule, negative rule also useful in today data mining task. In this paper we are proposing “A new method for generating all positive and negative Association Rules” (NRGA).NRGA generates all association rules which are hidden when we have applied Apriori Algorithm. For representation ...

The existence of many large transactions distributed databases with high data schemas, the centralized approach for mining association rules in such databases will not be feasible. Some distributed algorithms have been developed [FDM, CD], but none of them have considered the problem of data skews in distributed mining of association rules. The skewness of datasets reduces the workload balancin...