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Study on Efficient Way to Identify User Aware Rare Sequential Pattern Matching in Document Stream

Swati V. Mengje
2017 International Journal for Research in Applied Science and Engineering Technology  
Sequential Topic Patterns (URSTPs) in document streams on the Internet.  ...  In this paper, 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 pattern mining is an important problem in data mining, and has also been well studied so far. In the context of deterministic data, a comprehensive survey can be found.  ... 
doi:10.22214/ijraset.2017.2019 fatcat:s543g4e5gbaavcylwlqh73glyy

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  ...  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:10.5281/zenodo.3591065 fatcat:4kmd2knkvbdwjlqvhldmkylpnq

Mining User-aware Rare Sequential Topic Monitoring Pattern on Single Sign-on Document Stream in Multiple Web Applications

Mary Harin Fernandez F, Dept of Computer Science and Engineering, Jeppiaar SRR Engineering College, Chennai, India
2020 Informatica : Journal of Applied Machines Electrical Electronics Computer Science and Communication Systems  
In this paper, Sequential Topic Patterns (STPs) technique is used to formulate the issues of User-aware Rare Sequential Topic Patterns (URSTPs) mining in Internet document soure.  ...  The Sequential Subject Pattern (STP) is used to define and track Internet users' customised and abnormal behaviours.  ...  Sequential pattern mining, which has been studied so far, is the major difficulty in data mining. Prefixspan, Free Span and SPADE are some mining algorithms are proposed for Sequential Topic mining.  ... 
doi:10.47812/ijamecs2010104 fatcat:lflnms3rsjh2ljwijfmezxvxxa

Adding intelligence to your mobile device via on-device sequential pattern mining

Abhishek Mukherji, Vijay Srinivasan, Evan Welbourne
2014 Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing Adjunct Publication - UbiComp '14 Adjunct  
MSM is generic and can provide sequential patterns and predictions over multiple data streams, also allowing individual mobile applications to stream their own private data to mine sequential patterns.  ...  We introduce the idea of Mobile Sequence Mining (MSM) engine that automatically learns phone usage sequential patterns over the rich context data captured within the device.  ...  MSM is generic and can provide sequential patterns and predictions over multiple data streams, also allowing individual mobile applications to stream their own private data to mine sequential patterns.  ... 
doi:10.1145/2638728.2641285 dblp:conf/huc/MukherjiSW14 fatcat:yz6eyaon3nfqjkxmx65z7uzrqi

PrefixSpan based Pattern Mining using Time Sliding Weight from Streaming Data

Ji-Soo Kang, Ji-Won Baek, Kyungyong Chung
2020 IEEE Access  
Accordingly, sequential pattern mining for streaming data needs to delete old patterns in consideration of the data accumulation feature.  ...  In this way, it is possible to conduct realtime sequential pattern mining that fits time-series stream data. A.  ... 
doi:10.1109/access.2020.3007485 fatcat:7uynlihoobhvdipzvnjoxzgwqa

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.  ...  We present our method with its application to the detection and extraction of human sequential behavioral patterns over multiple multimodal data streams in human-robot interactions.  ...  ACKNOWLEDGMENT We thank Hong-Wei Shen and Amanda Favata for help with running subjects, Computational Cognition and Learning Lab members for help in coding data, and Henry Choi and Hong-Wei Shen for programming  ... 
doi:10.1109/devlrn.2011.6037334 dblp:conf/icdl-epirob/FrickerZY11 fatcat:ebxc6s2ie5hj7l35fps6myqenu

Mining spatio-temporal data

Gennady Andrienko, Donato Malerba, Michael May, Maguelonne Teisseire
2006 Journal of Intelligent Information Systems  
the data mining methods.  ...  The main impulse to research in this subfield of data mining comes from the large amount of & spatial data made available by GIS, CAD, robotics and computer vision applications, computational biology,  ...  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

Web Traffic Mining Using Neural Networks

Farhad F. Yusifov
2008 Zenodo  
In this paper, we propose a intelligent model to discover and analyze useful knowledge from the available Web log data.  ...  The task of mining useful information becomes more challenging when the Web traffic volume is enormous and keeps on growing.  ...  MINING WEB USAGE DATA Web usage mining is defined as the process of applying data mining techniques to the discovery of usage patterns from web logs data, to identify web users' behavior.  ... 
doi:10.5281/zenodo.1076858 fatcat:teyqz6uldjghnhlwfwic2v7fba

DSM-PLW: Single-pass mining of path traversal patterns over streaming Web click-sequences

Hua-Fu Li, Suh-Yin Lee, Man-Kwan Shan
2006 Computer Networks  
In this paper, we propose a projection-based, single-pass algorithm, called DSM-PLW (Data Stream Mining for Path traversal patterns in a Landmark Window), for online incremental mining of path traversal  ...  Mining Web click streams is an important data mining problem with broad applications.  ...  We would like to thank Blue Martini Software for contributing the KDD Cup 2000 data. The research is supported in part by the National Science Council, Project No.  ... 
doi:10.1016/j.comnet.2005.10.018 fatcat:cn2aghozazfwnibdvhveigc5yy

Contextualized Behavior Patterns for Ambient Assisted Living [chapter]

Paula Lago, Claudia Jiménez-Guarín, Claudia Roncancio
2015 Lecture Notes in Computer Science  
mining them on data streams.  ...  Proposed Algorithm In this section we first introduce the notion of a contextualized sequential pattern and then present the data structure and proposed algorithm for mining them from a data stream.  ... 
doi:10.1007/978-3-319-24195-1_10 fatcat:e5lecwb4dzdgfmgljd42mikcae

Automatic Modeling of Frequent Behaviors of Avatars and Players in a On Line Game

Chih-Ming Chiu, Shao-Shin Hung, Jyh-Jong Tsay
2011 International Journal of Virtual Reality  
To the best of our knowledge, little work builds character interaction model based on the data stream mining.  ...  Besides, we develop two efficient approaches for mining the behavior data to find the interesting behavior pattern for future prediction on responses of opponents.  ...  is called MSS-MB, for mining the set of frequent stream patterns over data streams with memory-bounded.  ... 
doi:10.20870/ijvr.2011.10.1.2795 fatcat:le4geebhdrc3deyvxe5tcotn4y

A study on video data mining

V. Vijayakumar, R. Nedunchezhian
2012 International Journal of Multimedia Information Retrieval  
Data mining is a process of extracting previously unknown knowledge and detecting the interesting patterns from a massive set of data.  ...  Compared to the mining of other types of data, video data mining is still in its infancy. There are many challenging research problems existing with video mining.  ...  Xie and Chang [106] investigated the pattern mining strategies in video streams.  ... 
doi:10.1007/s13735-012-0016-2 fatcat:xuuf3w3b2rfcxlyevzndz6v62e

Research Challenges for Data Mining in Science and Engineering [chapter]

Jiawei Han, Jing Gao
2008 Chapman & Hall/CRC Data Mining and Knowledge Discovery Series  
, and understanding of patterns and knowledge, (3) stream data mining, (4) mining moving object data, RFID data, and data from sensor networks, (5) spatiotemporal and multimedia data mining, (6) mining  ...  scale, either being stored in gigantic storage devices or flowing into and out of the system in the form of data streams.  ...  Automatic categorization of images and videos, classification of spatiotemporal data, finding frequent/sequential patterns and outliers, spatial collocation analysis, and many other tasks have been studied  ... 
doi:10.1201/9781420085877.pt1 fatcat:ljs2uybdofgkxfpouawfekdaz4

Efficient Updating of Discovered Patterns for Text Mining: A Survey

Anisha Radhakrishnan, Mathew Kurian
2012 International Journal of Computer Applications  
There are many techniques for mining the useful patterns from the text document. Researchers are still going in efficient updating of discovered pattern.  ...  Text mining is the techniques of retrieving interesting information from the text document. Through the devising of patterns, we can retrieve high-quality information.  ...  The main aim of the adaptive information filtering is to automatically retrieve the data stream to the topic specified by the user [4] .  ... 
doi:10.5120/9248-3412 fatcat:at5yg5umyzgdllhko5xc773d64

Temporal feature induction for baseball highlight classification

Michael Fleischman, Brandon Roy, Deb Roy
2007 Proceedings of the 15th international conference on Multimedia - MULTIMEDIA '07  
The method exploits techniques from temporal data mining to discover a codebook of temporal patterns that encode long distance dependencies and duration information.  ...  This paper presents a method, called temporal feature induction, which automatically mines complex temporal information from raw video for use in highlight classification.  ...  Temporal Data Mining Temporal patterns are mined from the multiple parallel streams abstracted from the raw video data.  ... 
doi:10.1145/1291233.1291305 dblp:conf/mm/FleischmanRR07 fatcat:cbrluxi6ivcmblf2ehaj2pglku
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