Data mining promises to discover valid and potentially useful patterns in data. Often, discovered patterns are not useful to the user. "Actionability" addresses this problem in that a pattern is deemed actionable if the user can act upon it in her favor. We introduce the notion of "action" as a domain-independent way to model the domain knowledge. Given a data set about actionable features and an utility measure, a pattern is actionable if it summarizes a population that can be acted upon towards a more promising population observed with a higher utility. We present several pruning strategies taking into account the actionability requirement to reduce the search space, and algorithms for mining all actionable patterns as well as mining the top k actionable patterns. We evaluate the usefulness of patterns and the focus of search on a real-world application domain.
Unexpected rules are interesting because they are either previously unknown or deviate from what prior user knowledge would suggest. In this paper, we study three important issues that have been previously ignored in mining unexpected rules. First, the unexpectedness of a rule depends on how the user prefers to apply the prior knowledge to a given scenario, in addition to the knowledge itself. Second, the prior knowledge should be considered right from the start to focus the search on unexpected rules. Third, the unexpectedness of a rule depends on what other rules the user has seen so far. Thus, only rules that remain unexpected given what the user has seen should be considered interesting. We develop an approach that addresses all three problems above and evaluate it by means of experiments focusing on finding interesting rules.
Abstract. Outlier analysis is an important task in data mining and has attracted much attention in both research and applications. Previous work on outlier detection involves different types of databases such as spatial databases, time series databases, biomedical databases, etc. However, few of the existing studies have considered spatial networks where points reside on every edge. In this paper, we study the interesting problem of distance-based outliers in spatial networks. We propose an efficient mining method which partitions each edge of a spatial network into a set of length d segments, then quickly identifies the outliers in the remaining edges after pruning those unnecessary edges which cannot contain outliers. We also present algorithms that can be applied when the spatial network is updating points or the input parameters of outlier measures are changed. The experimental results verify the scalability and efficiency of our proposed methods.
Data mining focuses on patterns that summarize the data. In this paper, we focus on mining patterns that could change the state by responding to opportunities of actions.
The efficient and accurate detection of foreign objects invading railway tracks holds paramount importance in safeguarding the safety of train operations. Focusing on the problem of the low efficiency of the existing foreign objects detection methods, this work proposes a fast railway foreign objects intrusion detection method based on cascaded convolution neural network and knowledge distillation. First, a two-stage cascade convolution neural network is built.The first stage can identify whether the railway images are intruded by foreign objects or not. This is achieved by a light weight image classification network.In the second stage,YOLOv3 is employed to classify and locate the objects in the intruded railway image. The use of lightweight classification network can reduce the use of the object detection network, thus improving the overall efficiency of the railway foreign objects intrusion detection method in this paper. Secondly, this paper employs the Overhaul knowledge distillation algorithm to train a lightweight network that is supervised by a larger network, so that the lightweight network constructed in this paper also has satisfying image classification performance. Finally, the YOLOv3 object detection network is used to detect the foreign object image classified by the first level network. The experimental results demonstrate that the accuracy of the image classification network proposed in this paper is competitive to the classical backbone network, and the FPS is about 50–70 higher than the comparison method.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.