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Rare pattern mining: challenges and future perspectives
2018
Complex & Intelligent Systems
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Mining of rare patterns although being subsided has proved to be of vital importance in many domains. ...
Extracting frequent patterns from databases has always been an imperative task for the data mining community. ...
, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. ...
doi:10.1007/s40747-018-0085-9
fatcat:y6mifzkvbzdtncvalcoageqovu
Real-Time Knowledge Discovery and Dissemination for Intelligence Analysis
2009
2009 42nd Hawaii International Conference on System Sciences
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This paper describes the issues and challenges for real-time knowledge discovery and then discusses approaches and challenges for real-time data mining and stream mining. ...
Our goal is to extract accurate information to support the emergency responder, the war fighter, as well as the intelligence analyst in a timely manner. ...
kinds of patterns in data streams. ...
doi:10.1109/hicss.2009.363
dblp:conf/hicss/ThuraisinghamKKCHS09
fatcat:euta372kxnfwngtpaf6gthsub4
SPEED : Mining Maxirnal Sequential Patterns over Data Strearns
2006
2006 3rd International IEEE Conference Intelligent Systems
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To the best of our knowledge this is the first approach defined for mining sequential patterns in streaming data. ...
In this paper we propose a new approach, called Speed (Sequential Patterns Efficient Extraction in Data streams), to identify frequent maximal sequential patterns in a data stream. ...
In [2] , authors also address sequential patterns mining however they focus on patterns across multiple data streams. ...
doi:10.1109/is.2006.348478
fatcat:5wmsplbwmfai7hanp4jt7ktcma
STRUCTURE DISCOVERY IN SEQUENTIALLY-CONNECTED DATA STREAMS
2006
International journal on artificial intelligence tools
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In contrast, we are working to develop data mining techniques to discover patterns consisting of complex relationships between entities. ...
in a single data increment, can be discovered. ...
The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of AFRL or the ...
doi:10.1142/s0218213006003041
fatcat:wyxzrgrvd5g7fgold7z7w5t3iq
Memory-adaptive high utility sequential pattern mining over data streams
2017
Machine Learning
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High utility sequential pattern (HUSP) mining has emerged as an important topic in data mining. ...
A number of studies have been conducted on mining HUSPs, but they are mainly intended for non-streaming data and thus do not take data stream characteristics into consideration. ...
Memory adaptive high utility sequential pattern mining In this section, we propose a single-pass algorithm named memory adaptive high utility sequential pattern mining over data streams (MAHUSP) for incrementally ...
doi:10.1007/s10994-016-5617-1
fatcat:vymcebqsq5bv7fyaa6lfwsxsae
i-MagNet: A real-time intelligent framework for finding specific needles from needle stacks
2015
2015 IFIP/IEEE International Symposium on Integrated Network Management (IM)
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Currently the volume of telecom network management data is expanding exponentially, mainly due to the explosive growth in the number of communicating devices along with the increase in heterogeneity of ...
In order to increase the efficiency of the Operations Support System (OSS) and gain in-depth understanding of the generic relationship between network entities, the monitoring data needs to undergo large-scale ...
Continuous Stream Mining Engine (CSM Engine) is a Java based Complex Event Processing (CEP) Engine for real-time event correlation finding across multiple events based on pre-defined rules (abstraction ...
doi:10.1109/inm.2015.7140391
dblp:conf/im/ZamanRQKMM15
fatcat:izvul7pwvzctnmhq6oswhjuaie
SeqStream: Mining Closed Sequential Patterns over Stream Sliding Windows
2008
2008 Eighth IEEE International Conference on Data Mining
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This paper studies the problem of mining closed sequential patterns over data stream sliding windows. ...
An efficient algorithm Se-qStream is developed to mine closed sequential patterns in stream windows incrementally, and various novel strategies are adopted in SeqStream to prune search space aggressively ...
Acknowledgements The authors would like to thank 5 http://fimi.cs.helsinki.fi/data/ anonymous reviewers for their valuable comments and suggestions. ...
doi:10.1109/icdm.2008.36
dblp:conf/icdm/ChangWYL08
fatcat:gxu6l3vbyjdsddcumgkiuxeyzq
Activity Recognition with Evolving Data Streams
2018
ACM Computing Surveys
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This paper surveys the two overlapped areas of research of activity recognition and data stream mining. ...
Categories of techniques are identified based on different features in both data streams and activity recognition. ...
In data stream mining, few studies have addressed the actual mining of sequential patterns in data streams. ...
doi:10.1145/3158645
fatcat:xsgcvmjy7rbifpfkmrclbhdwsa
A survey on learning from data streams: current and future trends
2012
Progress in Artificial Intelligence
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In this article we discuss the limitations of current machine learning and data mining algorithms. ...
Data streams produce huge amount of data that introduces new constraints in the design of learning algorithms: limited computational resources in terms of memory, cpu power, and communication bandwidth ...
Streams. ...
doi:10.1007/s13748-011-0002-6
fatcat:lk3mn36yvbf57p7u2ugy7onkum
Connected-Vehicles Applications Are Emerging [Connected Vehicles]
2016
IEEE Vehicular Technology Magazine
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Acknowledgements The views expressed here are purely those of the author and may not, under any circumstance, be regarded as an official position of the European Commission. ...
Therefore, handling sparsity and maintaining an adequate level sparsity in data stream mining applications help in improving the quality of knowledge patterns. ...
In Table 1 , the type of results in data stream mining is defined as approximate. ...
doi:10.1109/mvt.2015.2508322
fatcat:gtlzk33iovcqrfcebfsrjjdxoe
Efficient incremental mining of contrast patterns in changing data
2010
Information Processing Letters
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The existing incremental technique for mining contrast patterns [10] , performs well when changes of a single type occur in the input data, but has drawbacks when changes of multiple types simultaneously ...
LMDR processes the updates sequentially in four steps: It incrementally mines the patterns after each of the updates 1) ∇ n , followed by 2) ∆ n , then 3) ∇ p and then 4) ∆ p . ...
doi:10.1016/j.ipl.2009.10.012
fatcat:lvsmgfbebbbwzmw3kvhyzfe7jq
Incremental pattern discovery on streams, graphs and tensors
2008
SIGKDD Explorations
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Incremental pattern discovery targets at streaming applications where the data are arriving continuously in real-time. How to find patterns (main trends) in real-time? ...
In this thesis proposal, we first investigate a powerful data model tensor stream (TS) where there is one tensor per timestamp. ...
However, many applications have high order data. In theory, the generalization to tensors is not hard as described by Drineas et al. [15] . ...
doi:10.1145/1540276.1540284
fatcat:l7tkqca3gzdejhdktai2u6ouji
An Extensive Review of Significant Researches in Data Mining
2014
Research Journal of Applied Sciences Engineering and Technology
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In this study, we provide a comprehensive survey and study of various methods in existence for item set mining based on the utility and frequency and association rule mining based research works and also ...
An action that removes a few novel nontrivial data enclosed in large databases is defined as Data Mining. ...
In general sequential pattern mining, the generation order of data elements was considered to find sequential patterns. ...
doi:10.19026/rjaset.7.865
fatcat:nmzomgruzfgvlkzy3vtxe4ugre
Chronos
2014
Proceedings of the Ninth European Conference on Computer Systems - EuroSys '14
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The design of Chronos further explores the interesting interplay among locality, parallelism, and incremental computation in supporting common mining tasks on temporal graphs. ...
"bulk" operations on temporal graphs are scheduled to maximize the benefit of in-memory data locality. ...
This work has been partially supported by the National High-Tech Research and Development Plan (863 Project) 2012AA010903, as well as the National Science Foundation for Distinguished Young Scholars of ...
doi:10.1145/2592798.2592799
dblp:conf/eurosys/HanMLWYZPCC14
fatcat:xznh7loxfbgpddiqzjridgjpxa
Social Networking Data Research Using Frequent Pattern Mining and Machine Learning Data
2019
International Journal of Engineering and Advanced Technology
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The data are generated by the sources are very large in number with variety of form. These data are organized in to specific format in order to handle properly. ...
Data mining methods are addressed various problem during data extraction process to analytical process. The relevant data are extracted by applying pattern over the huge databases. ...
Sequential pattern mining is also applied for generating items sets by applying association rule in sequence order. ...
doi:10.35940/ijeat.f9352.088619
fatcat:7mfk3kmldzhclkt5a67astv7tm
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