| Title |
Misleading generalized itemset mining in the cloud |
| ID_Doc |
41539 |
| Authors |
Baralis, E; Cagliero, L; Cerquitelli, T; Chiusano, S; Garza, P; Grimaudo, L; Pulvirenti, F |
| Title |
Misleading generalized itemset mining in the cloud |
| Year |
2014 |
| Published |
|
| DOI |
10.1109/ISPA.2014.36 |
| Abstract |
In the era of smart cities huge data volumes are continuously generated and collected, thus prompting the need for efficient and distributed data mining approaches. Generalized itemset mining is an established data mining technique, which entails the discovery of multiple-level patterns hidden in the analyzed data by exploiting analyst-provided taxonomies. Among the generalized itemsets, the most peculiar high-level patterns are those with many contrasting correlations among items at different abstraction levels. They represent misleading situations that are worth analyzing separately by experts during manual inspection. This paper proposes a novel cloud-based service, named MGI-CLOUD, to efficiently mine misleading multiple-level patterns, i.e., theMisleading Generalized Itemsets, on a distributed computing environment. MGI-CLOUD consists of a set of distributed MapReduce jobs running in the cloud. As a case study, the system has been contextualized in a real-life scenario, i.e., the analysis of traffic law infractions committed in a smart city environment. The experiments, performed on real datasets, demonstrate the efficiency and effectiveness of MGI-CLOUD. |
| Author Keywords |
Generalized itemset mining; distributed computing model; cloud-based service; smart city |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000364951700027 |
| WoS Category |
Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications |
| Research Area |
Computer Science |
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