Achieving Approximate Soft Clustering in Data Streams
This addresses the need for efficient real-time clustering in streaming data applications, but it is incremental as it builds on existing k-means++ extensions.
The paper tackles the problem of soft clustering in data streams by proposing a one-pass streaming algorithm that approximates the soft k-means objective, achieving a pseudo-approximation with more than k centers and extending it to moving windows.
In recent years, data streaming has gained prominence due to advances in technologies that enable many applications to generate continuous flows of data. This increases the need to develop algorithms that are able to efficiently process data streams. Additionally, real-time requirements and evolving nature of data streams make stream mining problems, including clustering, challenging research problems. In this paper, we propose a one-pass streaming soft clustering (membership of a point in a cluster is described by a distribution) algorithm which approximates the "soft" version of the k-means objective function. Soft clustering has applications in various aspects of databases and machine learning including density estimation and learning mixture models. We first achieve a simple pseudo-approximation in terms of the "hard" k-means algorithm, where the algorithm is allowed to output more than $k$ centers. We convert this batch algorithm to a streaming one (using an extension of the k-means++ algorithm recently proposed) in the "cash register" model. We also extend this algorithm when the clustering is done over a moving window in the data stream.