In data stream ensemble classification， to make the classifiers adapt to the constantly changing data stream and adjust the weights of base classifiers to select an appropriate set of classifiers， an ensemble classification algorithm based on dynamic weighting function was proposed. Firstly， a new weighting function was proposed to adjust the weights of the base classifiers， and the classifiers were trained with constantly updated data blocks. Then a weight function was used to make a reasonable selection of candidate classifiers. Finally， the incremental nature of decision tree was applied to the base classifiers， and the classification of data stream was realized. Through a large amount of experiments， it is found that the performance of the proposed algorithm is not affected by block size. Compared with AUE2 algorithm， the average number of leaves is reduced by 681.3， the average number of nodes is reduced by 1 192.8， and the average depth of the tree is reduced by 4.42. At the same time， the accuracy is relatively improved and the time-consuming is reduced. Experimental results show that the algorithm can not only guarantee the accuracy but also save a lot of memory and time when classifying data stream.

High Utility Pattern Mining （HUPM） is one of the emerging data science research contents. The unit profit and number of items in the transaction database are considered to extract more useful information. The utility value of each item is assumed to be positive by the traditional HUPM methods， but in practical applications， the utility values of some data items may be negative （for example， the profit value of the product is negative due to a loss）， and the pattern mining with negative items is as important as the pattern mining with only positive terms. Firstly， the relevant concepts of HUPM were explained， and the examples of corresponding positive and negative utilities were given. Then， the HUPM methods were divided into positive and negative perspectives， among which the pattern mining methods with positive utility were further divided into dynamic and static database perspectives； the pattern mining methods with negative utility included priori-based， tree-based， utility list-based， and array-based key technologies. the HUPM methods were discussed and summarized from different aspects. Finally， the shortcomings of the existing HUPM methods and the next research directions were given.

In the traditional ensemble classification algorithm， the ensemble number is generally set to a fixed value， which may lead to a low classification accuracy. Aiming at this problem， an accuracy Climbing Ensemble Classification Algorithm （C-ECA） was proposed. Firstly， the base classifiers was no longer replaced the same number of base classifiers with the worst performance， but updated based on the accuracy in this algorithm， and then the optimal ensemble number was determined. Secondly， on the basis of C-ECA， a Dynamic Weighted Ensemble Classification Algorithm based on Climbing （C-DWECA） was proposed. When the base classifier was trained on the data stream with different features， the best weight of the base classifier was able to be obtained by a weighting function proposed in this algorithm， thereby improving the performance of the ensemble classifier. Finally， in order to detect the concept drift earlier and improve the final accuracy， Fast Hoffding Drift Detection Method （FHDDM） was adopted. Experimental results show that the accuracy of C-DWECA can reach up to 97.44%， and the average accuracy of the proposed algorithm is about 40% higher than that of Adaptable Diversity-based Online Boosting （ADOB） algorithm， and is also better than those of other comparison algorithms such as Leveraging Bagging （LevBag） and Adaptive Random Forest （ARF）.