A review of associative classification mining

Associative classification mining is a promising approach in data mining that utilizes the association rule discovery techniques to construct classification systems, also known as associative classifiers. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms employ several different rule discovery, rule ranking, rule pruning, rule prediction and rule evaluation methods. This paper focuses on surveying and comparing the state-of-the-art associative classification techniques with regards to the above criteria. Finally, future directions in associative classification, such as incremental learning and mining low-quality data sets, are also highlighted in this paper

Symbolic Computing with Incremental Mindmaps to Manage and Mine Data Streams - Some Applications

In our understanding, a mind-map is an adaptive engine that basically works incrementally on the fundament of existing transactional streams. Generally, mind-maps consist of symbolic cells that are connected with each other and that become either stronger or weaker depending on the transactional stream. Based on the underlying biologic principle, these symbolic cells and their connections as well may adaptively survive or die, forming different cell agglomerates of arbitrary size. In this work, we intend to prove mind-maps' eligibility following diverse application scenarios, for example being an underlying management system to represent normal and abnormal traffic behaviour in computer networks, supporting the detection of the user behaviour within search engines, or being a hidden communication layer for natural language interaction.Comment: 4 pages; 4 figure

Mining Meaning from Text by Harvesting Frequent and Diverse Semantic Itemsets

Abstract. In this paper, we present a novel and completely-unsupervised approach to unravel meanings (or senses) from linguistic constructions found in large corpora by introducing the concept of semantic vector. A semantic vector is a space-transformed vector where features repre-sent fine-grained semantic information units, instead of values of co-occurrences within a collection of texts. More in detail, instead of seeing words as vectors of frequency values, we propose to first explode words into a multitude of tiny semantic information retrieved from existing re-sources like WordNet and ConceptNet, and then clustering them into frequent and diverse patterns. This way, on the one hand, we are able to model linguistic data with a larger but much more dense and informa-tive semantic feature space. On the other hand, being the model based on basic and conceptual information, we are also able to generate new data by querying the above-mentioned semantic resources with the fea-tures contained in the extracted patterns. We experimented the idea on a dataset of 640 millions of triples subject-verb-object to automatically inducing senses for specific input verbs, demonstrating the validity and the potential of the presented approach in modeling and understanding natural language

Evolving temporal fuzzy association rules from quantitative data with a multi-objective evolutionary algorithm

A novel method for mining association rules that are both quantitative and temporal using a multi-objective evolutionary algorithm is presented. This method successfully identifies numerous temporal association rules that occur more frequently in areas of a dataset with specific quantitative values represented with fuzzy sets. The novelty of this research lies in exploring the composition of quantitative and temporal fuzzy association rules and the approach of using a hybridisation of a multi-objective evolutionary algorithm with fuzzy sets. Results show the ability of a multi-objective evolutionary algorithm (NSGA-II) to evolve multiple target itemsets that have been augmented into synthetic datasets