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Active Mining of Data Streams [chapter]

Wei Fan, Yi-an Huang, Haixun Wang, Philip S. Yu
2004 Proceedings of the 2004 SIAM International Conference on Data Mining  
In this paper, we propose a new concept of demand-driven active data mining. It estimates the error of the model on the new data stream without knowing the true class labels.  ...  Most previously proposed mining methods on data streams make an unrealistic assumption that "labelled" data stream is readily available and can be mined at anytime.  ...  Demand-driven Active Mining of Data Streams We are proposing a demand-driven active stream data mining process that solves the problems of passive stream data mining.  ... 
doi:10.1137/1.9781611972740.46 dblp:conf/sdm/FanHWY04 fatcat:rfe3kgzdubgtlf6xeabi6h3u2u

Activity Identification Utilizing Data Mining Techniques

Jae Lee, William Hoff
2007 2007 IEEE Workshop on Motion and Video Computing (WMVC'07)  
The proposed method utilizes various data mining techniques, including clustering, classification, and Markov model.  ...  We collect trajectories of moving objects of known activity types and build one Markov model for each activity type.  ...  We used the data mining software weka [12] to perform necessary clusterings and classifications.  ... 
doi:10.1109/wmvc.2007.4 fatcat:fvitko5byvdrdfmf2ky7vc2ypy

Mining Patterns of Activity from Video Data [chapter]

Michael C. Burl
2004 Proceedings of the 2004 SIAM International Conference on Data Mining  
The resulting track set, which encodes the positions, velocities, and appearances of the various objects as a function of time, are mined to answer user-generated queries that are potentially relevant  ...  The outputs from these steps can then be used as inputs to higher-level data mining processes.  ...  Introduction Data mining has been defined as the process of extracting implicit, nontrivial, previously unknown and potentially useful information from data in databases [8] .  ... 
doi:10.1137/1.9781611972740.61 dblp:conf/sdm/Burl04 fatcat:5kdit2ribveclo2windlygm6ke

Mining Exceptional Activity Patterns in Microstructure Data

Yuming Ou, Longbing Cao, Chao Luo, Li Liu
2008 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology  
The experiments on real-life stock data show that microstructure activity pattern analysis opens a new and effective means for crucially understanding and analysing market dynamics.  ...  The existing trading pattern analysis only focuses on interday data which discloses explicit and high-level market dynamics.  ...  Mining general patterns (MGP) In this step, data mining techniques are conducted on the microstructure sequences to discover exceptional microstructure activity patterns.  ... 
doi:10.1109/wiiat.2008.160 dblp:conf/webi/OuCLL08 fatcat:jkhomoupffauvls4l5s6ddiuha

Clustering Student Learning Activity Data

Haiyun Bian
2010 Educational Data Mining  
We show a variety of ways to cluster student activity datasets using different clustering and subspace clustering algorithms.  ...  Our test data contains 30 students with 16 activities, and 7 students failed this class. The final grade is the weighted average from the scores in all 16 activities.  ...  Clustering Student Activity Data We assume that datasets are in the following format: each row represents one student record, and each column measures one activity that students participate in.  ... 
dblp:conf/edm/Bian10 fatcat:lra6aghrzvh5hpv5aaqlgdbfk4

Active Data Discovery: Mining Unknown Data using Submodular Information Measures [article]

Suraj Kothawade, Shivang Chopra, Saikat Ghosh, Rishabh Iyer
2022 arXiv   pre-print
As a result, there has been a lot of work in designing active learning approaches for mining these rare data instances.  ...  In this work, we provide an active data discovery framework which can mine unknown data slices and classes efficiently using the submodular conditional gain and submodular conditional mutual information  ...  ADD: Our framework for Active Data Discovery In this section, we propose ADD, a novel active learning based framework for data discovery.  ... 
arXiv:2206.08566v1 fatcat:qycwycrbanhdphj4diovwdsmbm

Mining Smart Card Data for Travelers' Mini Activities [article]

Boris Chidlovskii
2017 arXiv   pre-print
We propose to mine the smart card data to extract the mini activities.  ...  We introduce the notion of mini activities the travelers do during the trips; they can explain the deviation of simulated trips from the observed trips.  ...  Section 2 presents the process of mining smart card data for travel demand modeling.  ... 
arXiv:1712.06935v1 fatcat:vlkyc6fmgjc2hm2mmce5lk2wxy

Mobile Activity Recognition Using Ubiquitous Data Stream Mining [chapter]

João Bártolo Gomes, Shonali Krishnaswamy, Mohamed M. Gaber, Pedro A. C. Sousa, Ernestina Menasalvas
2012 Lecture Notes in Computer Science  
The advantages of on-board data stream mining for mobile activity recognition are: i) personalisation of models built to individual users; ii) increased privacy as the data is not sent to an external site  ...  In this paper, we propose the Mobile Activity Recognition System (MARS) where for the first time the classifier is built on-board the mobile device itself through ubiquitous data stream mining in an incremental  ...  We would also like to thank to the FCT project Knowledge Discovery from Ubiquitous Data Streams (PTDC/EIA-EIA/98355/2008).  ... 
doi:10.1007/978-3-642-32584-7_11 fatcat:x5ig7y7wy5hh3hd262xgduvvmu

Applying Data Mining Techniques to Identify Suitable Activities

Yu-Fang Yeh, Ching-Pao Chang
2015 Mathematical Problems in Engineering  
In the approach, association rule mining and clustering technique are applied to analyze personal activity-physiological information.  ...  Identifying suitable physical activities is crucial for personal health management.  ...  This study proposes an approach to the construction of activity models; in the approach, a data mining technique is applied to personal physiological data for the construction of personal activity models  ... 
doi:10.1155/2015/618061 fatcat:4b7cpwomxfczvdzgqvktcittpq

STING+: an approach to active spatial data mining

Wei Wang, Jiong Yang, R. Muntz
1999 Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337)  
This paper introduces an active spatial data mining approach which extends the current spatial data mining algorithms to efficiently support user-defined triggers on dynamically evolving spatial data.  ...  Spatial data mining presents new challenges due to the large size of spatial data, the complexity of spatial data types, and the special nature of spatial access methods.  ...  Our focus in this paper is to extend current spatial data mining techniques to support user-defined triggers, i.e., active spatial data mining.  ... 
doi:10.1109/icde.1999.754914 dblp:conf/icde/WangYM99 fatcat:g4buk2jy3zazvjoym2chierhre

Mining data to find subsets of high activity

Dhammika Amaratunga, Javier Cabrera
2004 Journal of Statistical Planning and Inference  
Mining a structure activity database (and some other datasets) illustrates the effectiveness of the ARF procedure. We conclude by proposing a basic paradigm for data mining.  ...  A computational algorithm that makes ARF feasible for use even with very large data mining datasets is presented.  ...  Buja and Lee (1999)'s data mining criterion for classification, like ARF, focuses on successes, and was our inspiration for the H criterion.  ... 
doi:10.1016/j.jspi.2003.06.014 fatcat:zfbmstkx55fmpco7vcvtvnb7sm

Mining Impact-Targeted Activity Patterns in Imbalanced Data

Longbing Cao, Yanchang Zhao, Chengqi Zhang
2008 IEEE Transactions on Knowledge and Data Engineering  
T in separated data sets), and sequential impact-reversed activity patterns (both P ! T and P Q ! T are frequent).  ...  First, the complexities of mining imbalanced impact-targeted activities are analyzed. We then discuss strategies for constructing impact-targeted activity sequences.  ...  The imbalanced class distribution of the impacttargeted activity data distresses existing data mining approaches.  ... 
doi:10.1109/tkde.2007.190635 fatcat:uy6bou7khfb35fnaaens2uqkzy

Mining and Identifying Relationships Among Sequential Patterns in Multi-Feature, Hierarchical Learning Activity Data

Cheng Ye, John S. Kinnebrew, Gautam Biswas
2014 Educational Data Mining  
We apply this methodology to action data gathered from the Betty's Brain learning environment.  ...  This paper extends an exploratory sequence mining methodology for assessing and comparing students' learning behaviors by autonomously identifying abstraction levels in a hierarchical taxonomy of actions  ...  Sequence mining is widely used in extracting knowledge from databases of human-generated activity data.  ... 
dblp:conf/edm/YeKB14 fatcat:gguy7mkwxfczvgtj4m5arytvrm

Time Series Analysis of VLE Activity Data

Ewa Mlynarska, Pádraig Cunningham, Derek Greene
2016 Educational Data Mining  
We use these clusters to identify distinct activity patterns among students, such as Procrastinators, Strugglers, and Experts.  ...  We demonstrate that, by clustering activity profiles represented as time series using Dynamic Time Warping, we can uncover meaningful clusters of students exhibiting similar behaviors even in a sparsely-populated  ...  As a solution, we present a method for mining student activity on sparse data via Time Series Clustering.  ... 
dblp:conf/edm/MlynarskaCG16 fatcat:xmnt4bbxnbgzbcdzt4sn6ofe7y

Application of Data Mining methods in analysis of company's activity Использование методов Data Mining в анализе деятельности предприятия

Tyurina Dina N.
2013 Bìznes Inform  
It shows main advantages of application of Data Mining means in analysis of company's activity.  ...  The article considers expediency of application of Data Mining means along with traditional statistical methods of analysis of financial and economic activity of a company for revealing all possible factors  ...  It shows main advantages of application of Data Mining means in analysis of company's activity.  ... 
doaj:c21c801c0c8d4df59039e75d1d10f924 fatcat:3j4a2omf4zfwbdyud3h2drzkku
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