Data Stream Classification Based on an Associative Classifier
Abstract
Currently, the diversity of sources generating data in a massive online manner cause data streams to become part of many real work applications. Learning from a data stream is a very challenging task due
to the non-stationary nature of this type of data. Characteristics such as infinite length, concept drift, concept evolution, and recurrent concepts are the most common problems that need to be addressed by data stream learning algorithms. In this work an algorithm for data stream classification based on an associative classifier is presented. This proposal combines a clustering algorithm and the Na¨ıve Associative Classifier for Online Data (NACOD) to address this problem. A set of micro-clusters (MCs), a data structure that summarize the information of the current data, is used instead of storing the whole data. The MCs are continually updated with the arriving data, either to create new MCs or to update existing ones. The added MCs helps to deal with concept drift. To assess the performance of the proposed model, experiments were carried out on
3 data sets commonly used to evaluate data stream classification algorithms: KDD Cup 1999, Forest Cover Type and Staglog (Shuttle). Our model achieved higher accuracies than those achieved with algorithms such as data stream version of Na¨ıve Bayes and Hoeffding Tree, the average accuracies achieved were for KDDDcup 1999: 100%, Statlog (Shuttle): 99.01% and Forest Cover Type 70.44%.
to the non-stationary nature of this type of data. Characteristics such as infinite length, concept drift, concept evolution, and recurrent concepts are the most common problems that need to be addressed by data stream learning algorithms. In this work an algorithm for data stream classification based on an associative classifier is presented. This proposal combines a clustering algorithm and the Na¨ıve Associative Classifier for Online Data (NACOD) to address this problem. A set of micro-clusters (MCs), a data structure that summarize the information of the current data, is used instead of storing the whole data. The MCs are continually updated with the arriving data, either to create new MCs or to update existing ones. The added MCs helps to deal with concept drift. To assess the performance of the proposed model, experiments were carried out on
3 data sets commonly used to evaluate data stream classification algorithms: KDD Cup 1999, Forest Cover Type and Staglog (Shuttle). Our model achieved higher accuracies than those achieved with algorithms such as data stream version of Na¨ıve Bayes and Hoeffding Tree, the average accuracies achieved were for KDDDcup 1999: 100%, Statlog (Shuttle): 99.01% and Forest Cover Type 70.44%.
Keywords
Data stream classification; associative classifier; concept-drift