Outlier Detection with Streaming Dyadic Decomposition

Gupta, Chetan; Grossman, Robert L.

doi:10.1007/978-3-540-73435-2_7

Cited by 3 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A finely partitioned grid (large value of g) with smaller cells discovers clusters with more precise boundaries than those discovered in a grid with coarse granularity (Figure 5). Finer granularity grid allows multi-scale analysis with consequences on memory requirement (Gupta and Grossman, 2007). In a coarse granularity grid clustering quality suffers due to inclusion of larger sized data regions with grossly non-uniform distribution of data points (cells with solid dots in Figure 6).…”

Section: Grid-based Approachmentioning

confidence: 99%

Stream Clustering Algorithms: A Primer

Kaur

Bhatnagar

Chakravarthy

2015

Studies in Big Data

View full text Add to dashboard Cite

Abstract.Stream data has become ubiquitous due to advances in acquisition technology and pervades numerous applications. These massive data gathered as continuous flow, are often accompanied by dire need for real-time processing. One aspect of data streams deals with storage management and processing of continuous queries for aggregation. Another significant aspect pertains to discovery and understanding of hidden patterns to derive actionable knowledge using mining approaches. This chapter focuses on stream clustering and presents a primer of clustering algorithms in data stream environment.Clustering of data streams has gained importance because of its ability to capture natural structures from unlabeled, non-stationary data. Single scan of data, bounded memory usage, and capturing data evolution are the key challenges during clustering of streaming data. We elaborate and compare the algorithms on the basis of these constraints. We also propose a taxonomy of algorithms based on the fundamental approaches used for clustering. For each approach, a systematic description of contemporary, well-known algorithms is presented. We place special emphasis on synopsis data structure used for consolidating characteristics of streaming data and feature it as an important issue in design of a stream clustering algorithms. We argue that a number of functional and operational characteristics (e.g. quality of clustering, handling of outliers, number of parameters etc.) of a clustering algorithm are influenced by the choice of synopsis. A summary of clustering features that are supported by different algorithms is given. Finally, research directions for improvement in the usability of stream clustering algorithms are suggested.

show abstract

Section: Grid-based Approachmentioning

confidence: 99%