In this paper we study a new problem in temporal pattern mining: discovering frequent arrangements of temporal intervals. We assume that the database consists of sequences of even...
Panagiotis Papapetrou, George Kollios, Stan Sclaro...
Data mining has recently attracted attention as a set of efficient techniques that can discover patterns from huge data. More recent advancements in collecting massive evolving da...
It is often expensive to acquire data in real-world data mining applications. Most previous data mining and machine learning research, however, assumes that a fixed set of trainin...
We study the notion of learning in an oblivious changing environment. Existing online learning algorithms which minimize regret are shown to converge to the average of all locally...
We address the problem of online de-noising a stream of input points. We assume that the clean data is embedded in a linear subspace. We present two online algorithms for tracking ...