Improvements in the data partitioning approach for frequent itemsets mining

Frequent Itemsets mining is well explored for various data types, and its computational complexity is well understood. There are methods to deal effectively with computational problems. This paper shows another approach to further performance enhancements of frequent items sets computation. We have made a series of observations that led us to inventing data pre-processing methods such that the final step of the Partition algorithm, where a combination of all local candidate sets must be processed, is executed on substantially smaller input data. The paper shows results from several experiments that confirmed our general and formally presented observations.

Frequent detection of human rhinoviruses, paramyxoviruses, coronaviruses, and bocavirus during acute respiratory tract infections

Viruses are the major cause of pediatric acute respiratory tract infection (ARTI) and yet many suspected cases of infection remain uncharacterized. We employed 17 PCR assays and retrospectively screened 315 specimens selected by season from a predominantly pediatric hospital-based population. Before the Brisbane respiratory virus research study commenced, one or more predominantly viral pathogens had been detected in 15.2% (n = 48) of all specimens. The Brisbane study made an additional 206 viral detections, resulting in the identification of a microbe in 67.0% of specimens. After our study, the majority of microbes detected were RNA viruses (89.9%). Overall, human rhinoviruses (HRVs) were the most frequently identified target (n=140) followed by human adenoviruses (HAdVs; n = 25), human metapneumovirus (HMPV; n=18), human bocavirus (HBoV; n = 15), human respiratory syncytial virus (HRSV; n = 12), human coronaviruses (HCoVs; n = 11), and human herpesvirus-6 (n = 11). HRVs were the sole microbe detected in 37.8% (n = 31) of patients with suspected lower respiratory tract infection (LRTI). Genotyping of the HRV VP4/VP2 region resulted in a proposed subdivision of HRV type A into sublineages A1 and A2. Most of the genotyped HAdV strains were found to be type C. This study describes the high microbial burden imposed by HRVs, HMPV, HRSV, HCoVs, and the newly identified virus, HBoV on a predominantly paediatric hospital population with suspected acute respiratory tract infections and proposes a new formulation of viral targets for future diagnostic research studies.

A border-based approach for hiding sensitive frequent itemsets

Sharing data among organizations often leads to mutual benefit. Recent technology in data mining has enabled efficient extraction of knowledge from large databases. This, however, increases risks of disclosing the sensitive knowledge when the database is released to other parties. To address this privacy issue, one may sanitize the original database so that the sensitive knowledge is hidden. The challenge is to minimize the side effect on the quality of the sanitized database so that nonsensitive knowledge can still be mined. In this paper, we study such a problem in the context of hiding sensitive frequent itemsets by judiciously modifying the transactions in the database. To preserve the non-sensitive frequent itemsets, we propose a border-based approach to efficiently evaluate the impact of any modification to the database during the hiding process. The quality of database can be well maintained by greedily selecting the modifications with minimal side effect. Experiments results are also reported to show the effectiveness of the proposed approach. © 2005 IEEE