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Finding Frequent Patterns Using Length-Decreasing Support Constraints

Masakazu Seno, George Karypis
2005 Data mining and knowledge discovery  
Given a length-decreasing support constraint, LPMiner finds all the frequent itemset patterns from an itemset database, and SLPMiner finds all the frequent sequential patterns from a sequential database  ...  Ideally, we want to find all the frequent patterns whose support decreases as a function of their length without having to find many uninteresting infrequent short patterns.  ...  Ramesh Agarwal from IBM research for introducing us to the problem of pattern mining under length-decreasing support constraints. We also will like to thank Prof.  ... 
doi:10.1007/s10618-005-0364-0 fatcat:d24o4hkrrrbbdfzvkcjttwdjaa

SLPMiner: an algorithm for finding frequent sequential patterns using length-decreasing support constraint

M. Seno, G. Karypis
2002 IEEE International Conference on Data Mining, 2002. Proceedings.  
Ideally, we desire to have an algorithm that finds all the frequent patterns whose support decreases as a function of their length.  ...  In this paper we present an algorithm called SLPMiner, that finds all sequential patterns that satisfy a length-decreasing support constraint.  ...  Definition 4 (Sequential Pattern Mining with Length-Decreasing Support) Given a sequential database F and a length-decreasing support constraint A simple way of finding such sequential patterns is to use  ... 
doi:10.1109/icdm.2002.1183937 dblp:conf/icdm/SenoK02 fatcat:yxbftjpwsfhldmloabnonll2va

BAMBOO: Accelerating Closed Itemset Mining by Deeply Pushing the Length-Decreasing Support Constraint [chapter]

Jianyong Wang, George Karypis
2004 Proceedings of the 2004 SIAM International Conference on Data Mining  
Previous study has shown that mining frequent patterns with length-decreasing support constraint is very helpful in removing some uninteresting patterns based on the observation that short patterns will  ...  As a result, a more desirable pattern discovery task could be mining closed patterns under the length-decreasing support constraint.  ...  that finds all the closed itemsets satisfying the length-decreasing support constraint.  ... 
doi:10.1137/1.9781611972740.41 dblp:conf/sdm/WangK04 fatcat:mxnlchxirje6lhdldrgakuxug4

The Smallest Valid Extension-Based Efficient, Rare Graph Pattern Mining, Considering Length-Decreasing Support Constraints and Symmetry Characteristics of Graphs

Unil Yun, Gangin Lee, Chul-Hong Kim
2016 Symmetry  
support conditions, depending on lengths of graph patterns.  ...  Frequent graph mining has been proposed to find interesting patterns (i.e., frequent sub-graphs) from databases composed of graph transaction data, which can effectively express complex and large data  ...  Chul-Hong Kim investigated and reviewed references for graph theories and graph pattern mining applications to contribute to enhance the introduction and related work parts.  ... 
doi:10.3390/sym8050032 fatcat:w6cmt75hmrgcpnafi72ikzi2na

Mining Sequential Patterns for Interval Based Events by Applying Multiple Constraints

Kalaivany M, Uma V
2014 International Journal on Computational Science & Applications  
TPrefixSpan algorithm finds the relevant frequent patterns from the given sequential patterns formed using interval based events.  ...  Sequential pattern mining finds the frequent subsequences or patterns from the given sequences.  ...  Requirements: To find the length and minimum support: From the given sequence database find the length and set minimum support initially as 0.5.  ... 
doi:10.5121/ijcsa.2014.4406 fatcat:moofokw435hxxftdurcg6swsha

Preference-Based Frequent Pattern Mining

Moonjung Cho, Jian Pei, Haixun Wang, Wei Wang
2005 International Journal of Data Warehousing and Mining  
non-trivial and often tricky to specify appropriate support thresholds and proper constraints.  ...  Although there are many in-depth studies on efficient frequent pattern mining algorithms and constraint pushing techniques, the effectiveness of frequent pattern mining remains a serious concern: it is  ...  Such clusters are often hard to find by conventional frequent pattern mining since their supports are too low.  ... 
doi:10.4018/jdwm.2005100103 fatcat:k2y577azwnhwviksafs457dygm

PrefixSpan Algorithm for Finding Sequential Pattern with Various Constraints

Pratik Saraf, R. R Sedamkar, Sheetal Rathi
2015 International Journal of Applied Information Systems  
As the size of datasets increases the overall time for finding the sequential patterns also get increased.  ...  Through maximum prefix length parameter the length of prefix pattern is set which is helpful for running the algorithm on large datasets.  ...  Both the datasets are sequential datasets and are evaluated based on two parameters (minimum support and maximum prefix length) in order to find the frequent sequential patterns in time and memory efficient  ... 
doi:10.5120/ijais15-451380 fatcat:k2fxmdb64nfobidmbgjaanfp4u

Efficient closed pattern mining in the presence of tough block constraints

Krishna Gade, Jianyong Wang, George Karypis
2004 Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '04  
In recent years, various constrained frequent pattern mining problem formulations and associated algorithms have been developed that enable the user to specify various itemsetbased constraints that better  ...  However, developing computationally efficient algorithms to find these block constraints poses a number of challenges as unlike the different itemset-based constraints studied earlier, these block constraints  ...  Later the SVE property has been used to mine sequences and closed itemsets with length decreasing support constraints [26, 31] .  ... 
doi:10.1145/1014052.1014070 dblp:conf/kdd/GadeWK04 fatcat:q4p2dhf6fzgrbbxt3242wuh5qq

Finding Good Subtrees for Constraint Optimization Problems Using Frequent Pattern Mining

Hongbo Li, Jimmy Lee, He Mi, Minghao Yin
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
In this paper, we propose a method employing frequent pattern mining (FPM), a classic datamining technique, to find good subtrees for solving constraint optimization problems.  ...  Making good decisions at the top of a search tree is important for finding good solutions early in constraint optimization.  ...  This work is supported by the National Natural Science Foundation of China under Grants (61802056, 61976050, 61972384, 61972063) and the Fundamental Research Funds for the Central Universities under  ... 
doi:10.1609/aaai.v34i02.5518 fatcat:g226snj43nb2vgctv65dx6dmru

Constraint-Based Measures for DNA Sequence Mining using Group Search Optimization Algorithm

Kuruva Lakshmanna, Neelu Khare
2016 International Journal of Intelligent Engineering and Systems  
We first present the concept of prefix span, which detects the frequent DNA sequence. Based on this prefix tree, length and width constraints are applied to handle restrictions.  ...  In this paper, we propose a 3-step DNA sequence mining algorithm, called 3s-DNASM, incorporating prefix span, length and width constraints and group search optimization.  ...  Find length 1 sequential patterns Find length of sequence patterns for the DNA sequence database DB considering the minimum support that has been given.  ... 
doi:10.22266/ijies2016.0930.09 fatcat:k2aebqpcwjdsrhhx2sa5q5vlv4

Differentially private frequent sequence mining via sampling-based candidate pruning

Shengzhi Xu, Sen Su, Xiang Cheng, Zhengyi Li, Li Xiong
2015 2015 IEEE 31st International Conference on Data Engineering  
In particular, we use the noisy local support of candidate sequences in the sample databases to estimate which sequences are potentially frequent.  ...  To improve the accuracy of such private estimations, a sequence shrinking method is proposed to enforce the length constraint on the sample databases.  ...  Then, in the mining phase, we privately find frequent sequences in order of increasing length.  ... 
doi:10.1109/icde.2015.7113354 pmid:26973430 pmcid:PMC4788512 fatcat:2a2kzdgdyfhjxhoqag4t5cil6u

Efficient constraint-based Sequential Pattern Mining (SPM) algorithm to understand customers' buying behaviour from time stamp-based sequence dataset

Niti Ashish Kumar Desai, Amit Ganatra, Hsien-Tsung Chang
2015 Cogent Engineering  
Article attempts a solution through development of a SPM algorithm based on various constraints like Gap, Compactness, Item, Recency, Profitability and Length along with Frequency constraint.  ...  Conventional SPM algorithms worked purely on frequency identifying patterns that were more frequent but suffering from challenges like generation of huge number of uninteresting patterns, lack of user's  ...  Test III evaluates importance of individual constraint in form of pattern generation. Test IV tries to find out buying behaviour of customer using length constraint.  ... 
doi:10.1080/23311916.2015.1072292 fatcat:zmmms6e7mzhjbi4gpufw7mzk54

Survey on Constrained based Data Stream Mining

Lini SusanKurien, Sreekumar K, Minu KK
2014 International Journal of Computer Applications  
There are certain techniques to deal with data streams, in particular, finding the frequent or sequential patterns that occur repeatedly.  ...  These results retrieve huge number of patterns, which are hard to analyze and use them, also difficult to store these results and its intermediate results.  ...  When mining classification rules for documents, a user may be interested in only frequent patterns with at least 5 keywords, a typical length Super pattern Constraint To find the pattern that contains  ... 
doi:10.5120/18834-0348 fatcat:7pkf7x2o2nhpfl5g5beap6p4ee

Frequent Pattern Mining and Current State of the Art

Kalli Srinivasa Nageswara Prasad, S. Ramakrishna
2011 International Journal of Computer Applications  
There are two problems regarding this context, they are identifying all frequent item sets and to generate constraints from them.  ...  Relied on an experimental observation, which conclude that minimum support between 40 to 50% is sufficient to find frequent patterns.  ...  In the second stage the minimum support and sequence length 'k' is determined. The output of frequent mining algorithm is used as an important threshold in the outbreak detection tasks.  ... 
doi:10.5120/3114-4279 fatcat:nza37yy2prft3jrueownimthw4

Top-k sequence pattern mining with non-overlapping condition

Xin Chai, Dan Yang, Jingyu Liu, Yan Li, Youxi Wu
2018 Filomat  
We find the top k patterns of length len, and calculate the supports of the corresponding k × |Σ| super-patterns of length len + 1 to discover the new top k super-patterns with len + 1.  ...  Top-k pattern mining, which involves finding the most frequent k patterns, is an effective strategy, because the more frequently a pattern occurs, the more likely they are to be important for users.  ...  However, some useful patterns may be missed. On the contrary, when minsup decreases, more patterns will be frequent patterns.  ... 
doi:10.2298/fil1805703c fatcat:ahg4fyayxzdybbzf5sqiuv762y
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