IDEAS-1997-2021-Final-Programs

This document records the final program for each of the 26 meetings of the International Database and Engineering Application Symposium from 1997 through 2021. These meetings were organized in various locations on three continents. Most of the papers published during these years are in the digital libraries of IEEE(1997-2007) or ACM(2008-2021)

Efficient Incremental Subspace Clustering in Data Streams

Performing data mining tasks in streaming data is considered a challenging research direction, due to the continuous data evolution. In this work, we focus on the problem of clustering streaming time series, based on the sliding window paradigm. More specifically, we use the concept of α-clusters in each time instance separately. A subspace αcluster consists of a set of streams, whose value difference is less than α in a consecutive number of time instances (dimensions). The clusters can be continuously and incrementally updated as the streaming time series evolve. The proposed technique is based on a careful examination of pair-wise stream similarities for a subset of dimensions and then, it is generalized for more streams per cluster. Performance evaluation results show that the proposed pruning criteria are important for search space reduction, and that the cost of incremental cluster monitoring is computationally more efficient than reclustering.