2012/07/26 by Vaneet Aggarwal, Shankar Krishnan, Aggarwal, Vaneet +1
Computer Science · #Advanced Clustering Algorithms Research #Artificial Intelligence (cs.AI) #Bayesian Methods and Mixture Models #Data Stream Mining Techniques #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.1207.6199
openalex publication_date 2012/07/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.