Citizen scientists, who are volunteers from the community that participate as field assistants in scientific studies [3], enable research to be performed at much larger spatial and...
There is an increasingly pressing need, by several applications in diverse domains, for developing techniques able to index and mine very large collections of time series. Examples...
Alessandro Camerra, Themis Palpanas, Jin Shieh, Ea...
Abstract--Randomization is a general technique for evaluating the significance of data analysis results. In randomizationbased significance testing, a result is considered to be in...
-- Temporal data are time-critical in that the snapshot at each timestamp must be made available to researchers in a timely fashion. However, due to the limited data, each snapshot...
Ke Wang, Yabo Xu, Raymond Chi-Wing Wong, Ada Wai-C...
Dataset shift from the training data in a source domain to the data in a target domain poses a great challenge for many statistical learning methods. Most algorithms can be viewed ...
Financial forecasting is the basis for budgeting activities and estimating future financing needs. Applying machine learning and data mining models to financial forecasting is both...
Abstract--The Affinity Propagation (AP) clustering algorithm proposed by Frey and Dueck (2007) provides an understandable, nearly optimal summary of a data set. However, it suffers...
Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. In many cases, regression algori...