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Knowledge discovery from data streams

João Gama, Jesus Aguilar-Ruiz, João Gama, Jesus Aguilar-Ruiz
2007 Intelligent Data Analysis  
The rapid expansion in information science and technology in general and the complexity and volume of data in particular have introduced new challenges for the research community.  ...  Standard machine learning algorithms work on static data. Most of the times, all the data is loaded into memory and the learning task is solved by performing multiple scans over the training data.  ...  In the first paper, An Ensemble Classifier for Drifting Concepts, the author proposes a multiple model approach for detecting and reacting to concept drift.  ... 
doi:10.3233/ida-2007-11101 fatcat:kkmc3coipfhx5etpz4vozavwby

A survey on learning from data streams: current and future trends

João Gama
2012 Progress in Artificial Intelligence  
We identify the main issues and current challenges that emerge in learning from data streams that open research lines for further developments.  ...  In this article we discuss the limitations of current machine learning and data mining algorithms.  ...  Discovery from Ubiquitous Data Streams.  ... 
doi:10.1007/s13748-011-0002-6 fatcat:lk3mn36yvbf57p7u2ugy7onkum

SEQUENTIAL PATTERN MINING ALGORITHMS – RECENT TRENDS

Sandeep Mukherjee, Department of Home and Hill Affairs, West Bengal Kolkata, India
2021 International Journal of Advanced Research in Computer Science  
Sequential pattern mining is a technique of data mining whose objective is to identify statistically relevant patterns within a database with time-related data.  ...  At present, most of the real sequence databases are incremental in nature. So there is a need to explore incremental and distributed pattern mining algorithms.  ...  It finds statistically relevant patterns from sequential data.  ... 
doi:10.26483/ijarcs.v12i6.6779 fatcat:2oafxujf25anzouynqusm5xj4q

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.  ...  Our approach consists of a partitioning strategy for incoming streams of trajectories in order to reduce the trajectory size and represent trajectories as strings.  ...  The challenge posed by data stream systems and data stream mining is that, in many applications, data must be processed continuously, either because of real time requirements or simply because the stream  ... 
doi:10.1007/978-3-319-08407-7_4 fatcat:7xk36duuvnc77cc5g3h7avrcmi

Incremental Mining on Association Rules [chapter]

W.-G. Teng, M.-S. Chen
2005 Studies in Fuzziness and Soft Computing  
With the increasing use of the record-based databases whose data is being continuously added, recent important applications have called for the need of incremental mining.  ...  On the other hand, approaches to generate approximations from data streams have received a significant amount of research attention recently.  ...  In mining sequential patterns [4] , all the transactions of a customer can be viewed as a sequence together and the support for a sequential pattern is the fraction of customers whose purchasing sequences  ... 
doi:10.1007/11362197_6 fatcat:q7hxky35tfabtfsnzstjunu2nm

Pattern Discovery of User Interface Sequencing by Rehabilitation Clients with Cognitive Impairments

William N. Robinson, Ali Raza Syed, Arash Akhlaghi, Tianjie Deng
2012 2012 45th Hawaii International Conference on System Sciences  
This paper introduces theory and design of stream sequence-mining for UI event streams. 45th Hawaii International Conference on System Sciences 978-0-7695-4525-7/12 $26.00  ...  We demonstrate the use of sequence pattern mining as applied to monitoring the usage of emailing software by clients with cognitive impairments.  ...  The data windowing of stream mining limits analysis to data segments rather than the whole dataset. These scope limitations focus analysis on localized patterns and their incremental changes.  ... 
doi:10.1109/hicss.2012.467 dblp:conf/hicss/RobinsonSAD12 fatcat:uofjctlt65c7rcl57z6575owji

Efficient Classifier Generation over Stream Sliding Window using Associative Classification Approach

K. PrasannaLakshmi, C.R.K.Reddy C.R.K.Reddy
2015 International Journal of Computer Applications  
Mining associative rules generated on data streams for prediction has raised greater research interest in recent years.  ...  Associative classification mining has shown better performance over many former classification techniques in Data Mining and Data Stream Mining domains.  ...  ACKNOWLEDGMENTS The authors would like to thank the reviewers for helpful comments 8.  ... 
doi:10.5120/20280-1123 fatcat:4743p5jyajb2zd5p3tg6kfq5be

Memory-adaptive high utility sequential pattern mining over data streams

Morteza Zihayat, Yan Chen, Aijun An
2017 Machine Learning  
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.  ...  High utility sequential pattern (HUSP) mining has emerged as an important topic in data mining.  ...  First, we propose a novel method for incrementally mining HUSPs over a data stream.  ... 
doi:10.1007/s10994-016-5617-1 fatcat:vymcebqsq5bv7fyaa6lfwsxsae

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.  ...  Our approach consists of a partitioning strategy for incoming streams of trajectories in order to reduce the trajectory size and represent trajectories as strings.  ...  The challenge posed by data stream systems and data stream mining is that, in many applications, data must be processed continuously, either because of real time requirements or simply because the stream  ... 
doi:10.1145/2513591.2513653 dblp:conf/ideas/MasciariGZ13 fatcat:fouwnt72ozdhvpll56hl6si2pm

Contextualized Behavior Patterns for Ambient Assisted Living [chapter]

Paula Lago, Claudia Jiménez-Guarín, Claudia Roncancio
2015 Lecture Notes in Computer Science  
The tree is incrementally updated after a batch of data is processed with prefixSpan [5] to extract frequent sequences from the batch, and update nodes and counters in the tree.  ...  mining them on data streams.  ... 
doi:10.1007/978-3-319-24195-1_10 fatcat:e5lecwb4dzdgfmgljd42mikcae

Real-Time Knowledge Discovery and Dissemination for Intelligence Analysis

Bhavani M. Thuraisingham, Latifur Khan, Murat Kantarcioglu, Sonia Chib, Jiawei Han, Sang Hyuk Son
2009 2009 42nd Hawaii International Conference on System Sciences  
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.  ...  on k such precomputed and incrementally updated microclusters.  ... 
doi:10.1109/hicss.2009.363 dblp:conf/hicss/ThuraisinghamKKCHS09 fatcat:euta372kxnfwngtpaf6gthsub4

Mining High Utility Itemsets using Up-Tree Algorithm

Kalaiselvi S, S. Nithya Kalarani
2017 IJARCCE  
The situation may become worse when the database contains large number of long transactions or long high utility itemsets (HUIs).The algorithm used here is UP-Growth (Utility Pattern Growth) for mining  ...  The information of high utility itemsets is maintained in a special data structure named UP-Tree (Utility Pattern Tree) such that the candidate itemsets can be generated efficiently with only two scans  ...  On the other hand, incremental and interactive data mining provide the ability to use previous data structures and mining results in order to reduce unnecessary calculations when a database is updated,  ... 
doi:10.17148/ijarcce.2017.6444 fatcat:zesypgrbbvbfliuovn2r54e3ze

Frequent Pattern Mining over Streaming Data: From models to research challenges

A. Saad, Rashed Salem, Hatem Abdel-Kader
2021 IJCI. International Journal of Computers and Information  
Extracting frequent patterns from streaming data raises new challenges for the data mining community. We present an overview of the growing field of data streams.  ...  Finally, it summarizes the open issues and challenges to current existing approaches while handling and processing data streams in realworld applications.  ...  To handle the updating of new data streams during the process, mining from the data stream has to be an incremental process [13] .  ... 
doi:10.21608/ijci.2021.207862 fatcat:wts63vn43vccbla4wsk4uy6snq

STRUCTURE DISCOVERY IN SEQUENTIALLY-CONNECTED DATA STREAMS

JEFFREY COBLE, DIANE J. COOK, LAWRENCE B. HOLDER
2006 International journal on artificial intelligence tools  
We contrast two formulations of the change detection process and demonstrate the ability to identify salient changes along meaningful dimensions and recognize trends in a relational data stream.  ...  Our approach includes a mechanism for summarizing discoveries from previous data increments so that the globally best patterns can be computed by examining only the new data increment.  ...  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

Recent progress on selected topics in database research — A report by nine young Chinese researchers working in the United States

Chen Zhiyuan, Li Chen, Jian Pei, Yufei Tao, Wang Haixun, Wei Wang, Jiong Yang, Jun Yang, Donghui Zhang
2003 Journal of Computer Science and Technology  
The study on database technologies, or more generally, the technologies of data and information management, is an important and active research field.  ...  For the obvious reason, the authors are listed alphabetically, while the sections are arranged in the order of the author list.  ...  Incremental mining studies how to update the models/patterns by factoring in the incremental part of data.  ... 
doi:10.1007/bf02947114 fatcat:q5zryrqmuffargpgyqcscwyb5i
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