Spark solutions for discovering fuzzy association rules in Big Data

Discovering Interesting Association Rules in Medical Data

We are presently exploring the idea of discovering association rules in medical data. There are several technical aspects which make this problem challenging. In our case medical data sets are small, but have high dimensionality. Information content is rich: there exist numerical, categorical, time and even image attributes. Data records are generally noisy. We explain how to map medical data t...

Discovering fuzzy association rules using fuzzy partition methods

Fuzzy association rules described by the natural language are well suited for the thinking of human subjects and will help to increase the flexibility for supporting users in making decisions or designing the fuzzy systems. In this paper, a new algorithm named fuzzy grids based rules mining algorithm (FGBRMA) is proposed to generate fuzzy association rules from a relational database. The propos...

Fuzzy Data Mining for Discovering Changes in Association Rules over Time

Association rule mining is an important topic in data mining research. Many algorithms have been developed for such task and they typically assume that the underlying associations hidden in the data are stable over time. However, in real world domains, it is possible that the data characteristics and hence the associations change significantly over time. Existing data mining algorithms have not...

Adaptive fuzzy partitions for evolving association rules in big data stream

Discovering Multi-Level Association Rules using Fuzzy Hierarchies

In this paper, Fuzzy concept hierarchies are used for multi-level association rule mining from large datasets via Attribute-Oriented Induction approach [1]. In this the process of fuzzy hierarchical induction approach is used and extends it with two new characteristics which improve applicability of the original approach in data mining. The proposed drilling-down approach of fuzzy induction mod...