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The concept drift detection method is an online learner. Its main task is to determine the position of drifts in the data stream, so as to reset the classifier after detecting the drift to improve the learning performance, which is very important in practical applications such as user interest prediction or financial transaction fraud detection. A new level transition threshold parameter is proposed, and a piecewise weighting mechanism including "Stable Level-Warning Level-Drift Level" is innovatively introduced in concept drift detection. The instances in the window are weighted in levels, and it is applied to the double sliding window. Based on this, a multi-level weighted drift detection method(MWDDM) is proposed. Especially, two variants which are MWDDM_H and MWDDM_M are proposed basd on Hoeffding inequality and Mcdiarmid inequality respectively. Experiments on artificial datasets show that MWDDM can detect abrupt and gradual concept drift faster than any other comparison algorithms, while maintaining a low false positive ratio and false alarm rate. Experiments on real-world datasets show that MWDDM has the highest classification accuracy in most cases.

In the data stream scenarios such as the Internet of Things and network click streams, the high-speed, continuous, and endless characteristics of data stream make it a challenging task to quickly mine high utility itemsets in limited memory space. In the data stream scenario, the sliding window model has received extensive research and attention because of its emphasis on recent data. However, in the sliding window model, adjacent windows have a large number of communal batches resulting in a large set of identical itemsets in the resultsets of adjacent windows. At the same time, to provide users with a concise and lossless resultset, a novel algorithm is proposed for mining closed high utility itemsets from data streams, named FCHM-Stream. The algorithm devises a new utility list structure based on batch division. It presents a resultset maintenance strategy based on the skip-list structure to quickly and effectively update the resultset when the window slides which can effectively avoid repeated mining of redundant itemsets. Moreover, various experiments are carried out to compare our method with state-of-the-art algorithms both in static and stream environments. Extensive experimental results show that the proposed algorithm significantly reduces the running time compared to previous algorithms.

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