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Mining minimal distinguishing subsequence patterns with gap constraints

Xiaonan Ji, James Bailey, Guozhu Dong
2006 Knowledge and Information Systems  
Discovering contrasts between collections of data is an important task in data mining. In this paper, we introduce a new type of contrast pattern, called a Minimal Distinguishing Subsequence (MDS).  ...  One particularly important type of constraint that can be integrated into the mining process is the maximum gap constraint.  ...  References [16] and [9] consider sequential pattern mining with gap constraints.  ... 
doi:10.1007/s10115-006-0038-2 fatcat:xo4kxjkzofd25emn7bmcs5p24a

Mining Minimal Distinguishing Subsequence Patterns with Gap Constraints

Xiaonan Ji, J. Bailey, Guozhu Dong
Fifth IEEE International Conference on Data Mining (ICDM'05)  
Discovering contrasts between collections of data is an important task in data mining. In this paper, we introduce a new type of contrast pattern, called a Minimal Distinguishing Subsequence (MDS).  ...  One particularly important type of constraint that can be integrated into the mining process is the maximum gap constraint.  ...  References [16] and [9] consider sequential pattern mining with gap constraints.  ... 
doi:10.1109/icdm.2005.96 dblp:conf/icdm/JiBD05 fatcat:exwnza62lvh6vfulj3iry5mqbe

Risk prediction for acute hypotensive patients by using gap constrained sequential contrast patterns

Shameek Ghosh, Mengling Feng, Hung Nguyen, Jinyan Li
2014 AMIA Annual Symposium Proceedings  
In this work, we propose a sequential pattern mining approach which target novel and informative sequential contrast patterns for the detection of hypotension episodes.  ...  Sequential patterns can thus aid in the development of a powerful critical care knowledge discovery framework for facilitating novel patient treatment plans.  ...  with gap constraints.  ... 
pmid:25954447 pmcid:PMC4419954 fatcat:s2bgzxxmazbinnlkrboqjc2wou

Contrast Pattern Mining and Its Application for Building Robust Classifiers [chapter]

Kotagiri Ramamohanarao
2010 Lecture Notes in Computer Science  
Striking Two Birds with One Stone: Simultaneous Mining of Positive and Negative Spatial  ...  The ability to distinguish, differentiate and contrast between different data sets is a key objective in data mining.  ...  8 ) 8 Xiaonan Ji, James Bailey, Guozhu Dong: Mining Minimal Distinguishing Subsequence Patterns with Gap Constraints.  ... 
doi:10.1007/978-3-642-16108-7_5 fatcat:gdfnoamfebcubgzj2qj7s5zzqm

An Occurrence Based Approach to Mine Emerging Sequences [chapter]

Kang Deng, Osmar R. Zaïane
2010 Lecture Notes in Computer Science  
Evaluating against two mining algorithms based on support and no gap constraint subsequences, the experiments on two types of datasets show that the ESs fulfilling our selection criterions achieve a satisfactory  ...  By measuring ESs with their occurrences, introducing gap constraint and keeping the uniqueness of items, our ESs demonstrate desirable discriminative power.  ...  Acknowledgement The execution program and datasets for the iterative patterns were provided by the original author, Dr. Lo et al. We would like to acknowledge their help in this regard.  ... 
doi:10.1007/978-3-642-15105-7_22 fatcat:uk4nplhljbb77e3ihqk4p2yrgq

Efficiently Mining Closed Subsequences with Gap Constraints [chapter]

Chun Li, Jianyong Wang
2008 Proceedings of the 2008 SIAM International Conference on Data Mining  
Our extensive performance study shows that our approach is very efficient in mining frequent closed subsequences with gap constraints.  ...  In this paper we re-examine the closed sequential pattern mining problem by introducing the gap constraints.  ...  The work in [12] studies the problem of mining minimal distinguishing subsequence patterns with gap constraints.  ... 
doi:10.1137/1.9781611972788.28 dblp:conf/sdm/LiW08 fatcat:gkbqpxerdrcmdeacwm6ctxgqba

Efficient Mining of Contrast Patterns and Their Applications to Classification

K. Ramamohanarao, J. Bailey, Hongjian Fan
2005 2005 3rd International Conference on Intelligent Sensing and Information Processing  
Data mining is one of the most important areas in the 21 century with many wide ranging applications. These include medicine, finance, commerce and engineering.  ...  Pattern mining is amongst the most important and challenging techniques employed in data mining. Patterns are collections of items which satisfy certain properties.  ...  Work in [34] describes the ConSGapMiner algorithm, which is able to mine all minimal distinguishing subsequences according to a maximum gap constraint.  ... 
doi:10.1109/icisip.2005.1619410 fatcat:mkajvfymuzaqnfdgka3vqbbxwa

Hypotension Risk Prediction via Sequential Contrast Patterns of ICU Blood Pressure

Shameek Ghosh, Mengling Feng, Hung Nguyen, Jinyan Li
2016 IEEE journal of biomedical and health informatics  
Then, distinguishing subsequences are identified using the sequential contrast mining algorithm.  ...  These subsequences are used to predict the occurrence of an AHE in a future time window separated by a user-defined gap interval.  ...  : Towards finding the set of all gap-constrained contrast sequential patterns, we employ the ConSGapMiner algorithm [18] , which was earlier used to extract minimal distinguishing subsequences (MDS) with  ... 
doi:10.1109/jbhi.2015.2453478 pmid:26168449 pmcid:PMC5219944 fatcat:miikjtckbrglhklbxfivtb4ysy

A global Constraint for mining Sequential Patterns with GAP constraint [article]

Amina Kemmar and Samir Loudni and Yahia Lebbah and Patrice Boizumault and Thierry Charnois
2015 arXiv   pre-print
In this paper, we propose the global constraint GAP-SEQ enabling to handle SPM with or without gap constraint. GAP-SEQ relies on the principle of right pattern extensions.  ...  Sequential pattern mining (SPM) under gap constraint is a challenging task. Many efficient specialized methods have been developed but they are all suffering from a lack of genericity.  ...  [9] studied the problem of mining frequent patterns with gap constraints.  ... 
arXiv:1511.08350v1 fatcat:gkppz2komzdencycs6gjsipg5m

Protein Sequence Classification Through Relevant Sequence Mining and Bayes Classifiers [chapter]

Pedro Gabriel Ferreira, Paulo J. Azevedo
2005 Lecture Notes in Computer Science  
The features consist in the number and average length of the relevant subsequences shared with each of the protein families.  ...  We tackle the problem of sequence classification using relevant subsequences found in a dataset of protein labelled sequences. A subsequence is relevant if it is frequent and has a minimal length.  ...  −A n where A i are amino-acids and −x− gaps greater than p i and smaller than q i , then two types of patterns can be distinguished: -Rigid Gap Patterns only contain gaps with a fixed length, i.e. p i  ... 
doi:10.1007/11595014_24 fatcat:5hd3jnflsjgdporlnm5tk2gap4

Efficiency Analysis of ASP Encodings for Sequential Pattern Mining Tasks [article]

Thomas Guyet, René Quiniou
2017 arXiv   pre-print
Thus, ASP is a good candidate for implementing pattern mining with background knowledge, which has been a data mining issue for a long time.  ...  We propose encodings of the classical sequential pattern mining tasks within two representations of embeddings (fill-gaps vs skip-gaps) and for various kinds of patterns: frequent, constrained and condensed  ...  Fig. 9 9 Runtime for mining frequent patterns with four approaches: CPSM, CPSM-emb, ASP with fill-gaps, ASP with skip-gaps.  ... 
arXiv:1711.05090v1 fatcat:5e7k43bzf5b7bn4svmzycjhxja

Efficiency Analysis of ASP Encodings for Sequential Pattern Mining Tasks [chapter]

Thomas Guyet, Yves Moinard, René Quiniou, Torsten Schaub
2017 Studies in Computational Intelligence  
Thus, ASP is a good candidate for implementing pattern mining with background knowledge, which has been a data mining issue for a long time.  ...  We propose encodings of the classical sequential pattern mining tasks within two representations of embeddings (fill-gaps vs skip-gaps) and for various kinds of patterns: frequent, constrained and condensed  ...  Fig. 9 9 Runtime for mining frequent patterns with four approaches: CPSM, CPSM-emb, ASP with fill-gaps, ASP with skip-gaps.  ... 
doi:10.1007/978-3-319-65406-5_3 fatcat:oiey5s7d2nh6rgqelbq5jjp3fm

A Unified Framework for Frequent Sequence Mining with Subsequence Constraints

Kaustubh Beedkar, Rainer Gemulla, Wim Martens
2019 ACM Transactions on Database Systems  
In Section 3, we introduce subsequence predicates and formally define the problem of frequent sequence mining with general subsequence constraints.  ...  A variety of such subsequence constraints has been studied in the literature, including length, gap, span, regular-expression, and hierarchy constraints.  ...  For example, pattern expressions use uncaptured wildcards to express gap constraints (or the absence thereof); e.g., the pattern expressions for regular expression constraints with and without gaps at  ... 
doi:10.1145/3321486 fatcat:qefxp3kaq5fohcupid36aqczam

Constraint-based Sequential Pattern Mining with Decision Diagrams [article]

Amin Hosseininasab, Willem-Jan van Hoeve, Andre A. Cire
2018 arXiv   pre-print
sequential pattern mining algorithm.  ...  Constrained sequential pattern mining aims at identifying frequent patterns on a sequential database of items while observing constraints defined over the item attributes.  ...  We therefore evaluate MPP and PPICt for mining patterns with the following gap and maximum span constraints over the time attribute: 30 ≤ Cgap(time) ≤ 90, 900 ≤ Cspn(time) ≤ 3600.  ... 
arXiv:1811.06086v1 fatcat:yoivqkcutzgmzpb7tv4idtojly

Constraint-Based Sequential Pattern Mining with Decision Diagrams

Amin Hosseininasab, Willem-Jan van Hoeve, Andre A. Cire
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Constraint-based sequential pattern mining aims at identifying frequent patterns on a sequential database of items while observing constraints defined over the item attributes.  ...  sequential pattern mining algorithm.  ...  We therefore evaluate MPP and PPICt for mining patterns with the following gap and maximum span constraints over the time attribute: 30 ≤ Cgap(time) ≤ 90, 900 ≤ Cspn(time) ≤ 3600.  ... 
doi:10.1609/aaai.v33i01.33011495 fatcat:sts7wduau5fvtg5deejs5as6tu
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