Abstract
In this paper, a new algorithm for Data Streaming clustering is proposed, namely the SetClust algorithm. The Data Streaming clustering model focuses on making clustering of the data while it arrives, being useful in many practical applications. The proposed algorithm, unlike other streaming clustering algorithms, is designed to handle cases when there is no available a priori information about the number of clusters to be formed, having as a second objective to discover the best number of clusters needed to represent the points. The SetClust algorithm is based on structures for disjoint-set operations, making the concept of a cluster to be the union of multiple well-formed sets to allow the algorithm to recognize non-spherical patterns even in high dimensional points. This yields to quadratic running time on the number of formed sets. The algorithm itself can be interpreted as an efficient data structure for streaming clustering. Results of the experiments show that the proposed algorithm is highly suitable for clustering quality on well-spread data points.
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Notes
- 1.
These operations should be done online without traversing through all the elements.
- 2.
We need to calculate the new mean, standard deviation and the rest of the information in constant time.
- 3.
If we take advantage of the fact that only the last formed set can make instability, we can achieve an overall worst-case running time complexity of O(rd).
- 4.
As in the previous case, if we take advantage of the fact that only the last formed set can make instability, we can perform this operation in worst-case running time complexity of \(O(\alpha (r)rd)\), where \(\alpha \) is the inverse Ackerman function. For any practical situation, the function is never greater than 4.
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Campos, I., León, J., Campos, F. (2020). An Efficient Set-Based Algorithm for Variable Streaming Clustering. In: Lossio-Ventura, J.A., Condori-Fernandez, N., Valverde-Rebaza, J.C. (eds) Information Management and Big Data. SIMBig 2019. Communications in Computer and Information Science, vol 1070. Springer, Cham. https://doi.org/10.1007/978-3-030-46140-9_9
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