Software clustering algorithms presented in the literature rarely incorporate in the clustering process dynamic information, such as the number of function invocations during runtime. Moreover, the structure of a software system is often multi-layered, while existing clustering algorithms often create flat system decompositions. This paper presents a software clustering algorithm called MULICso...

We present a new approach for tracking evolving and noisy data streams by estimating clusters based on density, while taking into account the possibility of the presence of an unknown amount of outliers, the emergence of new patterns, and the forgetting of old patterns. keywords: evolving data streams, robust clustering, dynamic clustering, stream clustering, scalable clustering

Service-oriented computing (SOC) activates communication through web services to provide as a service for business applications in the service-oriented architecture (SOA). To make SOC successful, finding needed build system directly depending on collection of is critical confront. In this paper, authors planned clustering-based approach called dynamic clustering (DCLUS). The novelty DCLUS compa...

We develop a new method to find the number of volatility regimes in nonstationary financial time series by applying unsupervised learning its structure. use change point detection partition into locally stationary segments and then compute distance matrix between segment distributions. The are clustered learned discrete via an optimization routine. Using this framework, we determine clustering ...