Online Mining Changes of Items over Continuous Append-only and Dynamic Data Streams

Online mining changes over data streams has been recognized to be an important task in data mining. Mining changes over data streams is both compelling and challenging. In this paper, we propose a new, single-pass algorithm, called MFC-append (Mining Frequency Changes of append-only data streams), for discovering the frequent frequency-changed items, vibrated frequency changed items, and stable frequency changed items over continuous append-only data streams. A new summary data structure, called Change-Sketch, is developed to compute the frequency changes between two continuous data streams as fast as possible.s Moreover, a MFC-append-based algorithm, called MFC-dynamic (Mining Frequency Changes of dynamic data streams), is proposed to find the frequency changes over dynamic data streams. Theoretical analysis and experimental results show that our algorithms meet the major performance requirements, namely single-pass, bounded space requirement, and real-time computing, in mining data streams

Online Mining Changes of Items over Continuous Append-only and Dynamic Data Streams

Online mining changes over data streams has been recognized to be an important task in data mining. Mining changes over data streams is both compelling and challenging. In this paper, we propose a new, single-pass algorithm, called MFC-append (Mining Frequency Changes of append-only data streams), for discovering the frequent frequency-changed items, vibrated frequency changed items, and stable frequency changed items over continuous append-only data streams. A new summary data structure, called Change-Sketch, is developed to compute the frequency changes between two continuous data streams as fast as possible.s Moreover, a MFC-append-based algorithm, called MFC-dynamic (Mining Frequency Changes of dynamic data streams), is proposed to find the frequency changes over dynamic data streams. Theoretical analysis and experimental results show that our algorithms meet the major performance requirements, namely single-pass, bounded space requirement, and real-time computing, in mining data streams

Online mining changes of items over continuous append-only and dynamic data streams

Abstract: Online mining changes over data streams has been recognized to be an important task in data mining. Mining changes over data streams is both compelling and challenging. In this paper, we propose a new, single-pass algorithm, called MFC-append (Mining Frequency Changes of append-only data streams), for discovering the frequent frequency-changed items, vibrated frequency changed items, and stable frequency changed items over continuous append-only data streams. A new summary data structure, called Change-Sketch, is developed to compute the frequency changes between two continuous data streams as fast as possible. Moreover, a MFC-append-based algorithm, called MFC-dynamic (Mining Frequency Changes of dynamic data streams), is proposed to find the frequency changes over dynamic data streams. Theoretical analysis and experimental results show that our algorithms meet the major performance requirements, namely single-pass, bounded space requirement, and real-time computing, in mining data streams