Clustering Algorithm on Data Stream with Skew Distribution Based on Temporal Density
2010
To solve the problem of clustering this paper proposes a concept of temporal density, which reveals a set of mathematical properties, especially the incremental computation. A clustering algorithm named TDCA (temporal density based clustering algorithm) with time complexity of O(c×m×lgm) is created with a tree structure implemented for both storage and retrieve efficiency. TDCA is capable of capturing the temporal features of a data stream with skew data distribution either in real time or on demand. The experimental results show that TDCA is functionable and scalable.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
16
References
3
Citations
NaN
KQI