Pattern Mining as Abduction: From Snapshots to Spatio-Temporal Sequential Patterns

Abstract Patterns have been extensively used to extract hypernym relations from texts. The most popular patterns are Hearst’s patterns, formulated as regular expressions mainly based on lexical information. Experiences have reported good precision and low recall for such patterns. Thus, several approaches have been developed for improving recall. While these approaches perform better in terms of recall, it remains quite difficult to further increase recall without degrading precision. In this paper, we propose a novel 3-phase approach based on sequential pattern mining to improve pattern-based approaches in terms of both precision and recall by (i) using a rich pattern representation based on grammatical dependencies (ii) discovering new hypernym patterns, and (iii) extending hypernym patterns with anti-hypernym patterns to prune wrong extracted hypernym relations. The results obtained by performing experiments on three corpora confirm that using our approach, we are able to learn sequential patterns and combine them to outperform existing hypernym patterns in terms of precision and recall. The comparison to unsupervised distributional baselines for hypernym detection shows that, as expected, our approach yields much better performance. When compared to supervised distributional baselines for hypernym detection, our approach can be shown to be complementary and much less loosely coupled with training datasets and corpora.

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Efficient Discovery of Sequential Patterns from Event-Based Spatio-Temporal Data by Applying Microclustering Approach

Studies in Big Data - Intelligent Methods and Big Data in Industrial Applications ◽

10.1007/978-3-319-77604-0_14 ◽

2018 ◽

pp. 183-199

Author(s):

Piotr S. Macia̧g

Keyword(s):

Sequential Patterns ◽

Temporal Data ◽

Spatio Temporal ◽

Event Based

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Traversal Pattern Mining in Web Usage Data

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch119 ◽

2008 ◽

pp. 2004-2021

Author(s):

Jenq-Foung Yao ◽

Yongqiao Xiao

Keyword(s):

Pattern Mining ◽

Pattern Discovery ◽

Web Usage Mining ◽

Sequential Patterns ◽

Web Usage ◽

Web Logs ◽

Frequent Episodes ◽

Browsing Behavior ◽

The Web ◽

Usage Data

Web usage mining is to discover useful patterns in the web usage data, and the patterns provide useful information about the user’s browsing behavior. This chapter examines different types of web usage traversal patterns and the related techniques used to uncover them, including Association Rules, Sequential Patterns, Frequent Episodes, Maximal Frequent Forward Sequences, and Maximal Frequent Sequences. As a necessary step for pattern discovery, the preprocessing of the web logs is described. Some important issues, such as privacy, sessionization, are raised, and the possible solutions are also discussed.

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Sequential Pattern Mining Algorithm Based on Text Data: Taking the Fault Text Records as an Example

Sustainability ◽

10.3390/su10114330 ◽

2018 ◽

Vol 10 (11) ◽

pp. 4330 ◽

Cited By ~ 2

Author(s):

Xinglong Yuan ◽

Wenbing Chang ◽

Shenghan Zhou ◽

Yang Cheng

Keyword(s):

Time Series ◽

Pattern Mining ◽

Sequential Pattern Mining ◽

Sequential Pattern ◽

Fault Classification ◽

Sequential Patterns ◽

Series Data ◽

Similarity Measurement ◽

Text Similarity ◽

Text Data

Sequential pattern mining (SPM) is an effective and important method for analyzing time series. This paper proposed a SPM algorithm to mine fault sequential patterns in text data. Because the structure of text data is poor and there are many different forms of text expression for the same concept, the traditional SPM algorithm cannot be directly applied to text data. The proposed algorithm is designed to solve this problem. First, this study measured the similarity of fault text data and classified similar faults into one class. Next, this paper proposed a new text similarity measurement model based on the word embedding distance. Compared with the classic text similarity measurement method, this model can achieve good results in short text classification. Then, on the basis of fault classification, this paper proposed the SPM algorithm with an event window, which is a time soft constraint for obtaining a certain number of sequential patterns according to needs. Finally, this study used the fault text records of a certain aircraft as experimental data for mining fault sequential patterns. Experiment showed that this algorithm can effectively mine sequential patterns in text data. The proposed algorithm can be widely applied to text time series data in many fields such as industry, business, finance and so on.

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Hierarchical trajectory clustering for spatio-temporal periodic pattern mining

Expert Systems with Applications ◽

10.1016/j.eswa.2017.09.040 ◽

2018 ◽

Vol 92 ◽

pp. 1-11 ◽

Cited By ~ 23

Author(s):

Dongzhi Zhang ◽

Kyungmi Lee ◽

Ickjai Lee

Keyword(s):

Pattern Mining ◽

Periodic Pattern ◽

Trajectory Clustering ◽

Spatio Temporal

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Discovery of closed spatio-temporal sequential patterns from event data

Procedia Computer Science ◽

10.1016/j.procs.2019.09.226 ◽

2019 ◽

Vol 159 ◽

pp. 707-716

Author(s):

Piotr S. Maciąg ◽

Marzena Kryszkiewicz ◽

Robert Bembenik

Keyword(s):

Sequential Patterns ◽

Event Data ◽

Spatio Temporal

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Detecting Implicit Security Exceptions Using an Improved Variable-Length Sequential Pattern Mining Method

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194017500462 ◽

2017 ◽

Vol 27 (08) ◽

pp. 1235-1268

Author(s):

Jinfu Chen ◽

Saihua Cai ◽

Dave Towey ◽

Lili Zhu ◽

Rubing Huang ◽

...

Keyword(s):

Visual Inspection ◽

Pattern Mining ◽

Sequential Pattern Mining ◽

Variable Length ◽

Sequential Pattern ◽

Sequential Patterns ◽

Mining Method ◽

Security Testing ◽

String Searching ◽

Correct Execution

The process of component security testing can produce massive amounts of monitor logs. Current approaches to detect implicit security exceptions (those which cannot be identified by visual inspection alone) compare correct execution sequences with fixed patterns mined from the execution of sequential patterns in the monitor logs. However, this is not efficient and is not suitable for mining large monitor logs. To enable effective mining of implicit security exceptions from large monitor logs, this paper proposes a method based on improved variable-length sequential pattern mining. The proposed method first mines the variable-length sequential patterns from correct execution sequences and from actual execution sequences, thus reducing the number of patterns. The sequential patterns are then detected using the Sunday string-searching algorithm. We conducted an experimental study based on this method, the results of which show that the proposed method can efficiently detect the implicit security exceptions of components.

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