Data stream clustering by divide and conquer approach based on vector model

Journal of Big Data

Table 1 General group of methods for data stream clustering

Method	Pros	Cons
Condensation-based [5, 26–29]	Having summary of data (global view) Linear complexity Scalability Additive and subtractive property	Resource constraints Detecting only spherical shape Relearning Applicable in low dimension
Data sampling [4, 30]	Speed up Memory usage Low computational complexity	Low quality
Density –based [9, 15, 31–33]	Arbitrary shaped clusters	Density threshold must be determined Noise sensitivity Outlier sensitivity Applicable in low dimension Relearning
Grid-based [2, 19, 34]	Arbitrary shaped clusters High dimension [14]	Stability Relearning
Hierarchical structure [11, 19, 35, 36]	Support evolving and concept drift No need to determining extra parameters	Relearning Inflexibility