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Mining Frequent Itemsets: a Formal Unification [article]

Slimane Oulad-Naoui, Hadda Cherroun, Djelloul Ziadi
2020 arXiv   pre-print
In the last two decades, a great deal of work has been devoted to the algorithmic aspects of the Frequent Itemset (FI) Mining problem. We are motivated by the need for formal modeling in the field.  ...  Indeed, we encode the itemsets as words over an ordered alphabet, and state this problem by a formal series over the counting semiring (N,+,×,0,1), whose range constitutes the itemsets and the coefficients  ...  This model is based on formal series over the semiring (N, +, ×, 0, 1), whose the range constitutes the itemsets and the coefficients their supports.  ... 
arXiv:1502.02642v4 fatcat:5jryhi5fcvcudo2wti5dknnwbm

Unsupervised pattern mining from symbolic temporal data

Fabian Mörchen
2007 SIGKDD Explorations  
The mining paradigms and the robustness of many proposed approaches are compared to aid the selection of the appropriate method for a given problem.  ...  We present a unifying view of temporal concepts and data models in order to categorize existing approaches for unsupervised pattern mining from symbolic temporal data.  ...  A different approach to periodicity mining based on inter arrival times of a symbol is presented in [64] .  ... 
doi:10.1145/1294301.1294302 fatcat:rwcvkifhknh2reo6zp2tkco4vq

Efficient mining of understandable patterns from multivariate interval time series

Fabian Mörchen, Alfred Ultsch
2007 Data mining and knowledge discovery  
The search for coincidence and partial order in interval data can be formulated as instances of the well known frequent itemset problem.  ...  We present a new method for the understandable description of local temporal relationships in multivariate data, called Time Series Knowledge Mining (TSKM).  ...  We are also grateful for the constructive criticism of the reviewers for this paper and previous conference submissions.  ... 
doi:10.1007/s10618-007-0070-1 fatcat:j2ijrwv6tnag3gzeqypuhyn36i

Automated API Property Inference Techniques

Martin P. Robillard, Eric Bodden, David Kawrykow, Mira Mezini, Tristan Ratchford
2013 IEEE Transactions on Software Engineering  
In particular, we derive a classification and organization of over 60 techniques into five different categories based on the type of API property inferred: unordered usage patterns, sequential usage patterns  ...  Our survey provides a synthesis of this complex technical field along different dimensions of analysis: properties inferred, mining techniques, and empirical results.  ...  ACKNOWLEDGMENTS This work has been made possible by the generous support of the Alexander von Humboldt Foundation, the German Federal Ministry of Education and Research (BMBF) within EC SPRIDE and by the  ... 
doi:10.1109/tse.2012.63 fatcat:pmoh6iwdvjfunnk45rmlsqfgxa

The blind men and the elephant: on meeting the problem of multiple truths in data from clustering and pattern mining perspectives

Arthur Zimek, Jilles Vreeken
2013 Machine Learning  
Second, we relate a representative of these areas, subspace clustering, to pattern mining.  ...  In this position paper, we discuss how different branches of research on clustering and pattern mining, while rather different at first glance, in fact have a lot in common and can learn a lot from each  ...  The elephant was satisfied with a handful of peanuts.  ... 
doi:10.1007/s10994-013-5334-y fatcat:lmqlklqeevcmtmuq4urn74is6u

Mining interesting subgraphs by output space sampling

Mohammad Al Hasan
2010 SIGKDD Explorations  
In [52] , the authors propose a set of novel regression-based approaches to effectively summarize frequent itemset patterns.  ...  Itemset Mining The itemset mining problem is to discover frequently co-occurring sets of items (or attributes).  ... 
doi:10.1145/1882471.1882482 fatcat:zye25ck5wjfxdc3afpczjdutmq

An improved association-mining research for exploring Chinese herbal property theory: based on data of the Shennong's Classic of Materia Medica

Rui Jin, Zhi-jian Lin, Chun-miao Xue, Bing Zhang
2013 Journal of Integrative Medicine  
Aiming for a better understanding of Chinese herbal property theory (CHPT), this paper performed an improved association rule learning to analyze semistructured text in the book entitled Shennong's Classic  ...  CHPT were acquired and further elucidated: (1) the many-to-one mapping of herbal efficacy to herbal property; (2) the nonrandom overlap between the related efficacy of qi and flavor.  ...  Funding and acknowledgements Conflict of interests The authors declare that they have no conflict of interests.  ... 
doi:10.3736/jintegrmed2013051 pmid:24063783 fatcat:fbn36ktvt5fmbpad43dvorju5a

A survey of interestingness measures for knowledge discovery

2005 Knowledge engineering review (Print)  
It is a well known fact that the data mining process can generate many hundreds and often thousands of patterns from data.  ...  This article presents a review of the available literature on the various measures devised for evaluating and ranking the discovered patterns produced by the data mining process.  ...  The method suggested by the authors requires a two stage approach that initially finds all frequent itemsets using a minimum support level which is then compared against the frequent itemsets derived from  ... 
doi:10.1017/s0269888905000408 fatcat:7aiqi4oacvd4hd2sdt2cdqh3na

Local Suppression and Splitting Techniques for Privacy Preserving Publication of Trajectories

Manolis Terrovitis, Giorgos Poulis, Nikos Mamoulis, Spiros Skiadopoulos
2017 IEEE Transactions on Knowledge and Data Engineering  
for aggregate query answering and frequent subsets data mining.  ...  We study the problem of preserving user privacy in the publication of location sequences.  ...  use as a heuristic the result of mining for maximal frequent patterns, but their heuristic is not invoked frequently.  ... 
doi:10.1109/tkde.2017.2675420 fatcat:6yachptctndgveygxjixr3kmxe

A Roadmap for Web Mining: From Web to Semantic Web [chapter]

Bettina Berendt, Andreas Hotho, Dunja Mladenic, Maarten van Someren, Myra Spiliopoulou, Gerd Stumme
2004 Lecture Notes in Computer Science  
Association rules are based on the notion of "frequent itemset", i.e. a set of items occuring together in more data records than an externally specified frequency threshold.  ...  A survey of sequence mining research is incorporated in the literature overview of [18] . Rules derived from frequent sequences can be used to predict events from a given series of observations.  ... 
doi:10.1007/978-3-540-30123-3_1 fatcat:tb4oxi6dkbgypeoephofr2ewmi

Anytime Subgroup Discovery in High Dimensional Numerical Data

Romain Mathonat, Diana Nurbakova, Jean-Francois Boulicaut, Mehdi Kaytoue
2021 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA)  
To overcome such limitations, we propose MonteCloPi, an approach based on a bottom-up exploration of numerical patterns with a Monte Carlo Tree Search.  ...  When it comes to numerical data, most of the existing SD approaches perform data discretizations and thus suffer from information loss.  ...  They used a frequent pattern mining approach, which is a different problem: we want to be able to find discriminating patterns. Similarly, Batal et al.  ... 
doi:10.1109/dsaa53316.2021.9564223 fatcat:c4edik7xgbakbb545xspwdztfu

Classification of multivariate time series via temporal abstraction and time intervals mining

Robert Moskovitch, Yuval Shahar
2014 Knowledge and Information Systems  
data points into a series of symbolic time intervals; (2) mining these intervals to discover frequent temporal patterns, using Allen's 13 temporal relations; (3) using the patterns as features to induce  ...  We introduce a framework for classification of multivariate time series analysis, which implements three phases: (1) application of a temporal-abstraction process that transforms a series of raw time-stamped  ...  The authors also wish to acknowledge the useful comments of the anonymous reviewers, which have significantly improved this manuscript.  ... 
doi:10.1007/s10115-014-0784-5 fatcat:u4lp5ywtubgk3flhbcyfi2ry4m

Moving Objects Analytics: Survey on Future Location & Trajectory Prediction Methods [article]

Harris Georgiou, Sophia Karagiorgou, Yannis Kontoulis, Nikos Pelekis, Petros Petrou, David Scarlatti, Yannis Theodoridis
2018 arXiv   pre-print
We provide an extensive review of over 50 works, also proposing a novel taxonomy of predictive algorithms over moving objects.  ...  The tremendous growth of positioning technologies and GPS enabled devices has produced huge volumes of tracking data during the recent years.  ...  -Pattern-based approaches, inspired by the spatial data mining domain, exploit on data mining patterns (classification models, frequent / sequential patterns etc.) that are built upon the history of movements  ... 
arXiv:1807.04639v1 fatcat:lvje57kod5eldaplkl53wbwgti

AR-miner: mining informative reviews for developers from mobile app marketplace

Ning Chen, Jialiu Lin, Steven C. H. Hoi, Xiaokui Xiao, Boshen Zhang
2014 Proceedings of the 36th International Conference on Software Engineering - ICSE 2014  
To facilitate mobile app developers discover the most "informative" user reviews from a large and rapidly increasing pool of user reviews, we present "AR-Miner" -a novel computational framework for App  ...  via an intuitive visualization approach.  ...  This paper presented AR-Miner, a novel framework for mobile app review mining to facilitate app developers extract the most "informative" information from raw user reviews in app marketplace with minimal  ... 
doi:10.1145/2568225.2568263 dblp:conf/icse/ChenLHXZ14 fatcat:wol6vqtkurdejoiqdz55tm2dmm

Latent Association Mining in Binary Data [article]

Carson Mosso, Kelly Bodwin, Suman Chakraborty, Kai Zhang, Andrew B. Nobel
2021 arXiv   pre-print
The LAMB method is based on a simple threshold model in which the observed binary values represent a random thresholding of a latent continuous vector that may have a complex association structure.  ...  In this context we develop and investigate a method called Latent Association Mining for Binary Data (LAMB).  ...  Mining approximate frequent itemsets from noisy data. In Proceedings of the Fifth IEEE International Con- ference on Data Mining, ICDM '05, pages 721-724.  ... 
arXiv:1711.10427v2 fatcat:bichd32zmrcvndgj4ujc2g6gfe
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