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PrefixSpan based Pattern Mining using Time Sliding Weight from Streaming Data

Ji-Soo Kang, Ji-Won Baek, Kyungyong Chung
2020 IEEE Access  
Accordingly, the prefixSpan based pattern mining using time sliding weight effectively discovers a sequential pattern of stream data.  ...  The candidate patterns that are not deleted are extracted as final sequential patterns. If new data is input from the stream data, new transactions are generated and scanned.  ... 
doi:10.1109/access.2020.3007485 fatcat:7uynlihoobhvdipzvnjoxzgwqa

Identification of User Aware Rare Sequential Pattern in Document Stream An Overview

Rajeshri R. Shelke
2019 Zenodo  
Patterns URSTPs in document streams on the Internet.  ...  In order to characterize and detect personalized and abnormal behaviours of Internet users, we propose Sequential Topic Patterns STPs and formulate the problem of mining User aware Rare Sequential Topic  ...  The use of lexical patterns and anchor texts, respectively, can be considered as an approximation of within document and cross-document alias references.  ... 
doi:10.5281/zenodo.3591065 fatcat:4kmd2knkvbdwjlqvhldmkylpnq

BFSPMiner: an effective and efficient batch-free algorithm for mining sequential patterns over data streams

Marwan Hassani, Daniel Töws, Alfredo Cuzzocrea, Thomas Seidl
2017 International Journal of Data Science and Analytics  
Supporting sequential pattern mining from data streams is nowadays a relevant problem in the area of data stream mining research.  ...  This allows us, for instance, to discover frequent patterns that would be lost according to alternative batch-based stream mining processing models.  ...  Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecomm ons.org/licenses/by/4.0/), which permits unrestricted use, distribution  ... 
doi:10.1007/s41060-017-0084-8 dblp:journals/ijdsa/HassaniTCS19 fatcat:ikqs4e7ocvdydcm5en3lxfgih4

Sequential pattern mining of multimodal data streams in dyadic interactions

Damian Fricker, Hui Zhang, Chen Yu
2011 2011 IEEE International Conference on Development and Learning (ICDL)  
In this paper we propose a sequential pattern mining method to analyze multimodal data streams using a quantitative temporal approach.  ...  While the existing algorithms can only find sequential orders of temporal events, this paper presents a new temporal data mining method focusing on extracting exact timings and durations of sequential  ...  Continuous Interval-based Event Mining In this section, we propose a new algorithm, event space miner (ESM), used to data-mine interval-based event patterns from example sets.  ... 
doi:10.1109/devlrn.2011.6037334 dblp:conf/icdl-epirob/FrickerZY11 fatcat:ebxc6s2ie5hj7l35fps6myqenu

Memory-adaptive high utility sequential pattern mining over data streams

Morteza Zihayat, Yan Chen, Aijun An
2017 Machine Learning  
High utility sequential pattern (HUSP) mining has emerged as an important topic in data mining.  ...  The results show that MAHUSP effectively discovers useful and meaningful patterns in both cases.  ...  UI and US (Ahmed et al. 2010 ) extend traditional sequential pattern mining.  ... 
doi:10.1007/s10994-016-5617-1 fatcat:vymcebqsq5bv7fyaa6lfwsxsae

Efficient Way to Identify User Aware Rare Sequential Patterns in Document Streams

Swati V. Mengje, Prof. Rajeshri R. Shelke
2017 International Journal of Trend in Scientific Research and Development  
Patterns (URSTPs) in document streams on the Internet.  ...  In order to characterize and detect personalized and abnormal behaviors of Internet users, we propose Sequential Topic Patterns (STPs) and formulate the problem of mining Useraware Rare Sequential Topic  ...  Sequential pattern mining finds interesting patterns in sequence of sets. Mining sequential patterns has become an important data mining task with broad applications [9] .  ... 
doi:10.31142/ijtsrd101 fatcat:gugmkc5y4rhxllrpldpaotitfu

Anomalous Event Detection in Traffic Video Based on Sequential Temporal Patterns of Spatial Interval Events

2015 KSII Transactions on Internet and Information Systems  
In this paper, a Lossy Count based Sequential Temporal Pattern mining approach (LC-STP) is proposed for detecting spatio-temporal abnormal events (such as a traffic violation at junction) from sequences  ...  of video streams.  ...  Statistical results Traffic intersection scenario [38] Pedestrian Crossing Sequence [39] ϵ = 0.1% s = 1.0% Conclusion This paper has proposed a new approach based on frequent temporal pattern mining  ... 
doi:10.3837/tiis.2015.01.010 fatcat:w7mfslopivhx3poabipnvyebxa

Mining spatio-temporal data

Gennady Andrienko, Donato Malerba, Michael May, Maguelonne Teisseire
2006 Journal of Intelligent Information Systems  
Both the temporal and spatial dimensions add substantial complexity to data mining tasks.  ...  Spatio-temporal data mining is an emerging research area dedicated to the development and application of novel computational techniques for the analysis of large spatio-temporal databases.  ...  It shows that classical sequential pattern mining methods cannot be used in a data stream environment and introduces an algorithm for the approximate discovery of sequential patterns in data streams.  ... 
doi:10.1007/s10844-006-9949-3 fatcat:7zsbjq37djbmnehni2carp3hz4

Trajectory Data Pattern Mining [chapter]

Elio Masciari, Gao Shi, Carlo Zaniolo
2014 Lecture Notes in Computer Science  
We approach this problem as that of mining for frequent sequential patterns.  ...  We mine frequent trajectories using a sliding windows approach combined with a counting algorithm that allows us to promptly update the frequency of patterns.  ...  To address this problem, we uses an auxiliary array (aux) for each new pattern in the new slide.  ... 
doi:10.1007/978-3-319-08407-7_4 fatcat:7xk36duuvnc77cc5g3h7avrcmi

Investigating Order Information in API-Usage Patterns: A Benchmark and Empirical Study

Ervina Çergani, Sebastian Proksch, Sarah Nadi, Mira Mezini
2018 Proceedings of the 13th International Conference on Software Technologies  
sequence mining, which are used by a larger number of developers across code repositories.  ...  Our results show practical evidence that not only do partial-order patterns represent a generalized super set of sequential-order patterns, partial-order mining also finds additional patterns missed by  ...  The authors want to thank Raajay Viswanathan for the technical support with the episode mining algorithm, and Ulf Brefeld for the useful suggestions on the analyses of the data presented on this paper.  ... 
doi:10.5220/0006839000910102 dblp:conf/icsoft/CerganiPNM18 fatcat:eitwod3u3rgthch5l2w7mtz5wq

Sequential pattern mining from trajectory data

Elio Masciari, Gao Shi, Carlo Zaniolo
2013 Proceedings of the 17th International Database Engineering & Applications Symposium on - IDEAS '13  
We approach this problem as that of mining for frequent sequential patterns.  ...  We mine frequent trajectories using a sliding windows approach combined with a counting algorithm that allows us to promptly update the frequency of patterns.  ...  To address this problem, we uses an auxiliary array (aux) for each new pattern in the new slide.  ... 
doi:10.1145/2513591.2513653 dblp:conf/ideas/MasciariGZ13 fatcat:fouwnt72ozdhvpll56hl6si2pm

Anomalous Payload Detection System Using Analysis of Frequent Sequential Pattern

Jun Ma, Guanzhong Dai, Jing Zhou
2009 2009 Fifth International Conference on Information Assurance and Security  
Our results may leads to a possible solution for sequential frequent-pattern mining in dynamic streams, the Sliding window by pruning the excess of incoming data and dealing only with the trimmed data,  ...  The main issues include frequent-pattern mining and data-overload handling. Therefore, a mining algorithm together with Separate dedicated overload-handling mechanisms is proposed.  ...  Like data mining in traditional databases, the subjects of data-stream mining mainly include frequent itemsets/patterns, association rules (Agrawal and Srikant, 1994) , sequential rules, classification  ... 
doi:10.1109/ias.2009.34 dblp:conf/IEEEias/MaDZ09 fatcat:pbxxfwow5vbm5lh6cthytvxifi

StreamMinerA Classifier Ensemble-based Engine to Mine Concept-drifting Data Streams [chapter]

W FAN
2004 Proceedings 2004 VLDB Conference  
StreamMiner uses several techniques to support mining over data streams with possible concept-drifts. We demonstrate the following two key functionalities of StreamMiner: 1.  ...  We demonstrate StreamMiner, a random decision-tree ensemble based engine to mine data streams.  ...  The original source was modified to generate continuous data streams with drifting concepts.  ... 
doi:10.1016/b978-012088469-8/50121-2 fatcat:cq776ymannbhjk2magsv72aggm

Development of Decision Tree Algorithm for Mining Web Data Stream

Sheetal Sharma, Swati Singh
2016 International Journal of Computer Applications  
In this work we develop decision tree algorithm, which is efficient mining method to mine log files and extract knowledge from web data stream and generated training rules and Pattern which are helpful  ...  data mining set of computer instructions in order to present only rules and patterns.  ...  Moreover it required to authenticate the algorithm for that purposes we use a traditional algorithm for mining sequential pattern from web log data.  ... 
doi:10.5120/ijca2016908770 fatcat:vnvlqchjf5g6dovdza3hsv4yru

A Survey of Utility-Oriented Pattern Mining

Wensheng Gan, Chun-Wei Lin, Philippe Fournier-Viger, Han-Chieh Chao, Vincent Tseng, Philip Yu
2019 IEEE Transactions on Knowledge and Data Engineering  
The main purpose of data mining and analytics is to find novel, potentially useful patterns that can be utilized in real-world applications to derive beneficial knowledge.  ...  In recent years, there has been an increasing demand for utility-oriented pattern mining (UPM, or called utility mining).  ...  UL and US extend traditional sequential pattern mining (SPM). The utility of a sequential pattern is calculated in two ways.  ... 
doi:10.1109/tkde.2019.2942594 fatcat:nipxkmyfb5cyxh2662xbz6feo4
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