Resource-aware very fast K-Means for ubiquitous data stream mining

Developments in data streams, coupled with the growth in mobile and pervasive devices, have led to the emergence of Ubiquitous Data Mining (UDM). UDM aims to perform data stream mining in a ubiquitous environment with resource-constrained and/or mobile devices. Over the past few years, stream mining techniques have attracted the attention of the data mining community. However these techniques have not addressed the problems imposed by applying the mining technique in a ubiquitous environment. Algorithm Output Granularity (AOG) has been proposed as a generic approach to enable resource-awareness in data stream mining through adaptation. AOG has been applied to lightweight mining techniques and proved its efficiency. Due to the generality of the approach, we propose to apply AOG to an efficient stream clustering technique: Very Fast K-Means (VFKM). It is an extension of K-Means for data stream clustering. VFKM is able to deal with continuous data rather than a static dataset. In this paper, we propose and develop a resource-aware version of Very Fast K-Means to enable its operation for UDM applications. Our model for Resource-Aware Very Fast K-Means (RA-VFKM) is able to adapt to variations in memory availability on mobile devices. We have experimentally demonstrated that such an adaptation enables our RA-VFKM to converge and provide results in situations (such as critically low available memory) where VFKM tends to result in an execution failure.