RESEARCH ISSUES CONCERNING ALGORITHMS USED FOR OPTIMIZING THE DATA MINING PROCESS

In this paper, we depict some of the most widely used data mining algorithms that have an overwhelming utility and influence in the research community. A data mining algorithm can be regarded as a tool that creates a data mining model. After analyzing a set of data, an algorithm searches for specific trends and patterns, then defines the parameters of the mining model based on the results of this analysis. The above defined parameters play a significant role in identifying and extracting actionable patterns and detailed statistics. The most important algorithms within this research refer to topics like clustering, classification, association analysis, statistical learning, link mining. In the following, after a brief description of each algorithm, we analyze its application potential and research issues concerning the optimization of the data mining process. After the presentation of the data mining algorithms, we will depict the most important data mining algorithms included in Microsoft and Oracle software products, useful suggestions and criteria in choosing the most recommended algorithm for solving a mentioned task, advantages offered by these software products.data mining optimization, data mining algorithms, software solutions

Incremental Learning Method for Data with Delayed Labels

Most research on machine learning tasks relies on the availability of true labels immediately after making a prediction. However, in many cases, the ground truth labels become available with a non-negligible delay. In general, delayed labels create two problems. First, labelled data is insufficient because the label for each data chunk will be obtained multiple times. Second, there remains a problem of concept drift due to the long period of data. In this work, we propose a novel incremental ensemble learning when delayed labels occur. First, we build a sliding time window to preserve the historical data. Then we train an adaptive classifier by labelled data in the sliding time window. It is worth noting that we improve the TrAdaBoost to expand the data of the latest moment when building an adaptive classifier. It can correctly distinguish the wrong types of source domain sample classification. Finally, we integrate the various classifiers to make predictions. We apply our algorithms to synthetic and real credit scoring datasets. The experiment results indicate our algorithms have superiority in delayed labelling setting