Discovering sequential rental patterns by fleet tracking

© Springer International Publishing Switzerland 2015. As one of the most well-known methods on customer analysis, sequential pattern mining generally focuses on customer business transactions to discover their behaviors. However in the real-world rental industry, behaviors are usually linked to other factors in terms of actual equipment circumstance. Fleet tracking factors, such as location and usage, have been widely considered as important features to improve work performance and predict customer preferences. In this paper, we propose an innovative sequential pattern mining method to discover rental patterns by combining business transactions with the fleet tracking factors. A novel sequential pattern mining framework is designed to detect the effective items by utilizing both business transactions and fleet tracking information. Experimental results on real datasets testify the effectiveness of our approach

CCPM: A Scalable and Noise-Resistant Closed Contiguous Sequential Patterns Mining Algorithm

International audienceMining closed contiguous sequential patterns has been addressed in the literature only recently, through the CCSpan algorithm. CCSpan mines a set of patterns that contains the same information than traditional sets of closed sequential patterns, while being more compact due to the contiguity. Although CCSpan outperforms closed sequential pattern mining algorithms in the general case, it does not scale well on large datasets with long sequences. Moreover, in the context of noisy datasets, the contiguity constraint prevents from mining a relevant result set. Inspired by BIDE, that has proven to be one of the most efficient closed sequential pattern mining algorithm, we propose CCPM that mines closed contiguous sequential patterns, while being scalable. Furthermore , CCPM introduces usable wildcards that address the problem of mining noisy data. Experiments show that CCPM greatly outperforms CCSpan, especially on large datasets with long sequences. In addition, they show that the wildcards allows to efficiently tackle the problem of noisy data

Antigenic cross-reactivity between severe acute respiratory syndrome-associated coronavirus and human coronaviruses 229E and OC43

Cross-reactivity between antibodies to different human coronaviruses (HCoVs) has not been systematically studied. By use of Western blot analysis, indirect immunofluorescence assay (IFA), and enzyme-linked immunosorbent assay (ELISA), antigenic cross-reactivity between severe acute respiratory syndrome (SARS)-associated coronavirus (SARS-CoV) and 2 HCoVs (229E and OC43) was demonstrated in immunized animals and human serum. In 5 of 11 and 10 of 11 patients with SARS, paired serum samples showed a ≥4-fold increase in antibody titers against HCoV-229E and HCoV-OC43, respectively, by IFA. Overall, serum samples from convalescent patients who had SARS had a 1-way cross-reactivity with the 2 known HCoVs. Antigens of SARS-CoV and HCoV-OC43 were more cross-reactive than were those of SARS-CoV and HCoV-229E. © 2005 by the Infectious Diseases Society of America. All rights reserved.published_or_final_versio