Event Mining for Explanatory Modeling

2021 ◽  
Author(s):  
Laleh Jalali ◽  
Ramesh Jain
Keyword(s):  
Event Mining

Event Mining Through Clustering

2014 ◽  
Vol 23 (1) ◽  
pp. 59-73
Author(s):  
E. Umamaheswari ◽  
T.V. Geetha
Keyword(s):  
Clustering Algorithm ◽  
Semantic Representation ◽  
Clustering Algorithms ◽  
Event Semantics ◽  
Information Retrieval Evaluation ◽  
Event Mining ◽  
Unique Word ◽  
Event Clustering ◽  
Cluster Efficiency ◽  
Single Cluster

AbstractTraditional document clustering algorithms consider text-based features such as unique word count, concept count, etc. to cluster documents. Meanwhile, event mining is the extraction of specific events, their related sub-events, and the associated semantic relations from documents. This work discusses an approach to event mining through clustering. The Universal Networking Language (UNL)-based subgraph, a semantic representation of the document, is used as the input for clustering. Our research focuses on exploring the use of three different feature sets for event clustering and comparing the approaches used for specific event mining. In our previous work, the clustering algorithm used UNL-based event semantics to represent event context for clustering. However, this approach resulted in different events with similar semantics being clustered together. Hence, instead of considering only UNL event semantics, we considered assigning additional weights to similarity between event contexts with event-related attributes such as time, place, and persons. Although we get specific events in a single cluster, sub-events related to the specific events are not necessarily in a single cluster. Therefore, to improve our cluster efficiency, connective terms between two sentences and their representation as UNL subgraphs were also considered for similarity determination. By combining UNL semantics, event-specific arguments similarity, and connective term concepts between sentences, we were able to obtain clusters for specific events and their sub-events. We have used 112 000 Tamil documents from the Forum for Information Retrieval Evaluation data corpus and achieved good results. We have also compared our approach with the previous state-of-the-art approach for Router-RCV1 corpus and achieved 30% improvements in precision.

scholarly journals Anomaly detection and event mining in cold forming manufacturing processes

2020 ◽  
Author(s):  
Diego Nieves Avendano ◽  
Daniel Caljouw ◽  
Dirk Deschrijver ◽  
Sofie Van Hoecke
Keyword(s):  
Anomaly Detection ◽  
Missing Values ◽  
Acoustic Emissions ◽  
Irregular Sampling ◽  
Predictive Maintenance ◽  
Data Driven ◽  
Normal Operation ◽  
Cold Forming ◽  
Event Prediction ◽  
Event Mining

Abstract Predictive maintenance is one of the main goals within the Industry 4.0 trend. Advances in data-driven techniques offer new opportunities in terms of cost reduction, improved quality control, and increased work safety. This work brings data-driven techniques for two predictive maintenance tasks: anomaly detection and event prediction, applied in the real-world use case of a cold forming manufacturing line for consumer lifestyle products by using acoustic emissions sensors in proximity of the dies of the press module. The proposed models are robust and able to cope with problems such as noise, missing values, and irregular sampling. The detected anomalies are investigated by experts and confirmed to correspond to deviations in the normal operation of the machine. Moreover, we are able to find patterns which are related to the events of interest.

An Approach to Event Mining Based on Massive Online News

2018 ◽  
Author(s):  
Piaopiao Zheng ◽  
Jian Wan ◽  
Huayou Si ◽  
Shuangshuang Zhang ◽  
Wenpeng Tao
Keyword(s):  
Online News ◽  
Event Mining
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