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Mining Top-k Sequential Patterns in Database Graphs:A New Challenging Problem and a Sampling-based Approach [article]

Mingtao Lei, Lingyang Chu, Zhefeng Wang
2018 arXiv   pre-print
Our goal is to find the top-k frequent sequential patterns in the sequence database induced from a database graph. We prove that this problem is #P-hard.  ...  To tackle this problem, we propose an efficient two-step sampling algorithm that approximates the top-k frequent sequential patterns with provable quality guarantee.  ...  First, we formulate the problem of mining top-k sequential patterns in database graphs.  ... 
arXiv:1805.03320v1 fatcat:esiui5kgtvfdxiaxktgqppgxj4

Efficiently Approximating Top-k Sequential Patterns in Transactional Graphs

Mingtao Lei, Xi Zhang, Jincui Yang, Binxing Fang
2019 IEEE Access  
To efficiently approximate the top-k patterns, we propose a Parallelized Sampling-based Approach For Mining Top-k Sequential Patterns, PSMSP, which involves two key techniques: (a) a parallelized unbiased  ...  INDEX TERMS Graph mining, sequential pattern mining, sampling.  ...  ACKNOWLEDGMENT This paper was presented in part at the 24th International Conference on Database Systems for Advanced Applications (DASFAA 2019).  ... 
doi:10.1109/access.2019.2916811 fatcat:ul42l7gyzfehth4z2lyl6cui5q

Frequent pattern mining: current status and future directions

Jiawei Han, Hong Cheng, Dong Xin, Xifeng Yan
2007 Data mining and knowledge discovery  
Abundant literature has been dedicated to this research and tremendous progress has been made, ranging from efficient and scalable algorithms for frequent itemset mining in transaction databases to numerous  ...  research frontiers, such as sequential pattern mining, structured pattern mining, correlation mining, associative classification, and frequent pattern-based clustering, as well as their broad applications  ...  For mining top-k most frequent closed patterns, a TFP algorithm ) is proposed to discover top-k closed frequent patterns of length no less than min_l.  ... 
doi:10.1007/s10618-006-0059-1 fatcat:fpblaafhurfbtiimurret4idde

Efficient Analysis of Pattern and Association Rule Mining Approaches

Thabet Slimani, Amor Lazzez
2014 International Journal of Information Technology and Computer Science  
algorithms for frequent itemset mining in transaction databases to complex algorithms, such as sequential pattern mining, structured pattern mining, correlation mining.  ...  The most recognized data mining tasks are the process of discovering frequent itemsets, frequent sequential patterns, frequent sequential rules and frequent association rules.  ...  The proposed algorithm allows to mine the top-k sequential rules from sequence databases, where k is the number of sequential rules to be found and is set by the user.  ... 
doi:10.5815/ijitcs.2014.03.09 fatcat:azuo5zey35flrc3disyiersjnu

A Graph-Based Differentially Private Algorithm for Mining Frequent Sequential Patterns

Miguel Nunez-del-Prado, Yoshitomi Maehara-Aliaga, Julián Salas, Hugo Alatrista-Salas, David Megías
2022 Applied Sciences  
Sequential pattern mining is a promising approach for discovering temporal regularities in huge and heterogeneous databases.  ...  In this paper, we propose a differential privacy graph-based technique for publishing frequent sequential patterns.  ...  the top-k most frequent patterns.  ... 
doi:10.3390/app12042131 fatcat:rj4u66mj3ve6rloyzb3vvhlrnm

High Utility Itemset Mining with Top-k CHUD (TCHUD) Algorithm

Anu Augustin, Vince Paul, Vishnu G.
2017 International Journal of Computer Applications  
This one is not a new concept and is derived from frequent itemset mining. Here we proposes an algorithm for mining closed high utility itemset using top-k algorithm.  ...  Both the concept of closed high utility itemset and top-k mining are existing. The new concept is that integrating the merits of them together.  ...  Top-k frequent pattern mining is explained in [5] .Top-k with effective threshold raising strategies are detailed in [8] .In [10] top-k sequential patterns are mined.  ... 
doi:10.5120/ijca2017913813 fatcat:mbz7u35lorf6dclt2enqx5mggq

Sequential Patterns Postprocessing for Structural Relation Patterns Mining

Jing Lu, Weiru Chen, Osei Adjei, Malcolm Keech
2008 International Journal of Data Warehousing and Mining  
Sequential patterns mining is an important data-mining technique used to identify frequently observed sequential occurrence of items across ordered transactions over time.  ...  A corresponding data-mining method based on sequential patterns postprocessing is proposed and shown to be effective in the search for concurrent patterns.  ...  Let I={i 1 ,i 2 ,…,i l } be a set of l items and let TDB=<T 1 ,T 2 ,…,T k > be a transaction database, where T j (1≤j≤k) is a transaction which contains a set of items in I.  ... 
doi:10.4018/jdwm.2008070105 fatcat:4mplgpf6yvalpod7ilhrybb4ui

Survey on Sequential Pattern Mining Algorithms

V. ChandraShekharRao, P. Sammulal
2013 International Journal of Computer Applications  
Sequential pattern mining is a significant data-mining method for determining time-related behavior in sequence databases.  ...  Although there have been many recent studies on the sequential patterns in static database.  ...  In a topdown approach the subsets of sequential patterns can be mined by constructing the corresponding set of projected databases and mining each recursively from top to bottom. Anti-Monotone Vs.  ... 
doi:10.5120/13301-0782 fatcat:eee6r7dtbffmfphbb4y4shh3be

Semantic annotation of frequent patterns

Qiaozhu Mei, Dong Xin, Hong Cheng, Jiawei Han, Chengxiang Zhai
2007 ACM Transactions on Knowledge Discovery from Data  
Using frequent patterns to analyze data has been one of the fundamental approaches in many data mining applications.  ...  Research in frequent pattern mining has so far mostly focused on developing efficient algorithms to discover various kinds of frequent patterns, but little attention has been paid to the important next  ...  pattern; xifeng yan sequential pattern; jiawei han T gspan graph-base substructure pattern mine T mine close relational graph connect constraint T clospan mine close sequential pattern large database  ... 
doi:10.1145/1297332.1297335 fatcat:uk6jlav3q5gcxixsyq5sj7lgxy

Rare pattern mining: challenges and future perspectives

Anindita Borah, Bhabesh Nath
2018 Complex & Intelligent Systems  
Extracting frequent patterns from databases has always been an imperative task for the data mining community.  ...  Mining of rare patterns although being subsided has proved to be of vital importance in many domains.  ...  distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecomm, which permits unrestricted use, distribution, and reproduction in  ... 
doi:10.1007/s40747-018-0085-9 fatcat:y6mifzkvbzdtncvalcoageqovu

Concurrency In Web Access Patterns Mining

Jing Lu, Malcolm Keech, Weiru Chen
2009 Zenodo  
An important technique to discover user access and navigation trails is based on sequential patterns mining.  ...  This paper proposes a novel model called Web Access Patterns Graph (WAP-Graph) to represent all of the access patterns from web mining graphically.  ...  For example, frequent itemset mining [13] aims to find frequent itemsets in a transaction database and sequential patterns mining [5, 6, 14] aims to find sub-sequences that appear frequently in a sequence  ... 
doi:10.5281/zenodo.1074754 fatcat:holownkbbffzzlha34vqncrtrm

A Proficient Approach of Incremental Algorithm for Frequent Pattern Mining

Endu Duneja, A.K. Sachan
2012 International Journal of Computer Applications  
The proposed algorithm can discover sequential frequent pattern itemsets in incremental database.  ...  We developed new method that considers sequential data mining of marketing websites as an effective tool that participates in having well-structured websites.  ...  To compute the set of sequential patterns in the updated database, we want to avoid counting everything from the scratch.  ... 
doi:10.5120/7467-0596 fatcat:3xjfbnav7zdhbccgmqg6efo364

ALPINE: Anytime Mining with Definite Guarantees [article]

Qiong Hu, Tomasz Imielinski
2016 arXiv   pre-print
Thus, it is very attractive for extremely long mining tasks with very high dimensional data (for example in genetics) because it can offer intermediate meaningful and complete results.  ...  ALPINE is to our knowledge the first anytime algorithm to mine frequent itemsets and closed frequent itemsets.  ...  In top-k mining, we select the Seq-Miner [13] that mines the top-k frequent patterns sequentially without any minimum support.  ... 
arXiv:1610.07649v1 fatcat:65tjp3znljds7fpjbcdztmo4ve

Revised Plwap Tree With Non-Frequent Items For Mining Sequential Pattern

R. Vishnu Priya, A. Vadivel
2011 Zenodo  
Sequential pattern mining is a challenging task in data mining area with large applications. One among those applications is mining patterns from weblog.  ...  While mining sequential patterns, the links related to the nonfrequent items are not considered.  ...  The sequential patterns are mined using Level-wise search. The sequential pattern of length 'k' candidate set are generated from the previously generated (k+1) patterns.  ... 
doi:10.5281/zenodo.1073589 fatcat:wvw6jwttnvb6roerlo6ur2h7ne

TKFIM: Top-K frequent itemset mining technique based on equivalence classes

Saood Iqbal, Abdul Shahid, Muhammad Roman, Zahid Khan, Shaha Al-Otaibi, Lisu Yu
2021 PeerJ Computer Science  
Furthermore, the results are compared with state-of-the-art techniques such as Top-k miner and Build Once and Mine Once (BOMO).  ...  The proposed procedure does not miss any FIs; thus, accurate frequent patterns are mined.  ...  was also funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-track Research ACM SIGKDD international conference on Knowledge discovery and data mining  ... 
doi:10.7717/peerj-cs.385 pmid:33817031 pmcid:PMC7959650 fatcat:ktbyfmfczvbe5oztoluqwut44u
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