Requirements for clustering data streams
D Barbará - ACM sIGKDD Explorations Newsletter, 2002 - dl.acm.org
ACM sIGKDD Explorations Newsletter, 2002•dl.acm.org
Scientific and industrial examples of data streams abound in astronomy, telecommunication
operations, banking and stock-market applications, e-commerce and other fields. A
challenge imposed by continuously arriving data streams is to analyze them and to modify
the models that explain them as new data arrives. In this paper, we analyze the
requirements needed for clustering data streams. We review some of the latest algorithms in
the literature and assess if they meet these requirements.
operations, banking and stock-market applications, e-commerce and other fields. A
challenge imposed by continuously arriving data streams is to analyze them and to modify
the models that explain them as new data arrives. In this paper, we analyze the
requirements needed for clustering data streams. We review some of the latest algorithms in
the literature and assess if they meet these requirements.
Scientific and industrial examples of data streams abound in astronomy, telecommunication operations, banking and stock-market applications, e-commerce and other fields. A challenge imposed by continuously arriving data streams is to analyze them and to modify the models that explain them as new data arrives. In this paper, we analyze the requirements needed for clustering data streams. We review some of the latest algorithms in the literature and assess if they meet these requirements.