Assistant Professor, University of South Florida
M.S. Thesis
As a popular data mining task, frequent pattern mining has been proven to be help- ful for non-experts. For example, mining frequent purchased products helps store managers increase sales. As another example, finding popular courses assists uni- versity administrators arrange courses to avoid schedule conflicts. However, many data mining researchers have focused on improving… Continue reading Delivering Scalable Frequent Pattern Mining for Non-Expert Data Miners
BigDAS 2015
Frequent itemset mining discovers implicit, previously unknown and potentially useful knowledge—in the form of frequent itemsets—from data. For example, discovery of frequently purchased merchandise products reveals customer purchase patterns, which help store managers about their business strategies and promotional tactics. These, in turn, help increase profits of the stores. As another example, discovery of… Continue reading FIMaaS: Scalable Frequent Itemset Mining-as-a-Service on Cloud for Non-Expert Miners
KES 2015
In the current era of Big data, high volumes of valuable data can be generated at a high velocity from high-varieties of data sources in various real-life applications ranging from sensor networks to social networks, from bio-informatics to chemical informatics. In addition, Big data are also available in business, education, engineering, finance, healthcare, scientific,… Continue reading Edge-based mining of frequent subgraphs from graph streams
