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Статті в журналах з теми "Approximate Mining"

Автор: Grafiati

Опубліковано: 11 березня 2023

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1

Livshits, Ester, Alireza Heidari, Ihab F. Ilyas, and Benny Kimelfeld. "Approximate denial constraints." Proceedings of the VLDB Endowment 13, no. 10 (June 2020): 1682–95. http://dx.doi.org/10.14778/3401960.3401966.

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The problem of mining integrity constraints from data has been extensively studied over the past two decades for commonly used types of constraints, including the classic Functional Dependencies (FDs) and the more general Denial Constraints (DCs). In this paper, we investigate the problem of mining from data approximate DCs, that is, DCs that are "almost" satisfied. Approximation allows us to discover more accurate constraints in inconsistent databases and detect rules that are generally correct but may have a few exceptions. It also allows to avoid overfitting and obtain constraints that are more general, more natural, and less contrived. We introduce the algorithm ADCMiner for mining approximate DCs. An important feature of this algorithm is that it does not assume any specific approximation function for DCs, but rather allows for arbitrary approximation functions that satisfy some natural axioms that we define in the paper. We also show how our algorithm can be combined with sampling to return highly accurate results considerably faster.

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2

Yip, Kelly K., and David A. Nembhard. "Mining approximate sequential patterns with gaps." International Journal of Data Mining, Modelling and Management 7, no. 2 (2015): 108. http://dx.doi.org/10.1504/ijdmmm.2015.069249.

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3

Combi, Carlo, and Pietro Sala. "Mining approximate interval-based temporal dependencies." Acta Informatica 53, no. 6-8 (September 14, 2015): 547–85. http://dx.doi.org/10.1007/s00236-015-0246-x.

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4

Chen, Yan, and Aijun An. "Approximate Parallel High Utility Itemset Mining." Big Data Research 6 (December 2016): 26–42. http://dx.doi.org/10.1016/j.bdr.2016.07.001.

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5

Su, Na, Zhe Hui Wu, Ji Min Liu, Tai An Liu, Xin Jun An, and Chang Qing Yan. "Mining Approximate Frequent Itemsets over Data Streams." Applied Mechanics and Materials 685 (October 2014): 536–39. http://dx.doi.org/10.4028/www.scientific.net/amm.685.536.

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Анотація:

This paper proposes a method based on Lossy Counting to mine frequent itemsets. Logarithmic tilted time window is adopted to emphasize the importance of recent data. Multilayer count queue framework is used to avoid the counter overflowing and query top-Kitemsets quickly using a index table.

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6

Silvestri, Claudio, and Salvatore Orlando. "Approximate mining of frequent patterns on streams." Intelligent Data Analysis 11, no. 1 (March 15, 2007): 49–73. http://dx.doi.org/10.3233/ida-2007-11104.

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7

McCoy, Corren G., Michael L. Nelson, and Michele C. Weigle. "Mining the Web to approximate university rankings." Information Discovery and Delivery 46, no. 3 (August 20, 2018): 173–83. http://dx.doi.org/10.1108/idd-05-2018-0014.

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Purpose The purpose of this study is to present an alternative to university ranking lists published in U.S. News & World Report, Times Higher Education, Academic Ranking of World Universities and Money Magazine. A strategy is proposed to mine a collection of university data obtained from Twitter and publicly available online academic sources to compute social media metrics that approximate typical academic rankings of US universities. Design/methodology/approach The Twitter application programming interface (API) is used to rank 264 universities using two easily collected measurements. The University Twitter Engagement (UTE) score is the total number of primary and secondary followers affiliated with the university. The authors mine other public data sources related to endowment funds, athletic expenditures and student enrollment to compute a ranking based on the endowment, expenditures and enrollment (EEE) score. Findings In rank-to-rank comparisons, the authors observed a significant, positive rank correlation (τ = 0.6018) between UTE and an aggregate reputation ranking, which indicates UTE could be a viable proxy for ranking atypical institutions normally excluded from traditional lists. Originality/value The UTE and EEE metrics offer distinct advantages because they can be calculated on-demand rather than relying on an annual publication and they promote diversity in the ranking lists, as any university with a Twitter account can be ranked by UTE and any university with online information about enrollment, expenditures and endowment can be given an EEE rank. The authors also propose a unique approach for discovering official university accounts by mining and correlating the profile information of Twitter friends.

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8

Mazlack, Lawrence J. "Approximate reasoning applied to unsupervised database mining." International Journal of Intelligent Systems 12, no. 5 (May 1997): 391–414. http://dx.doi.org/10.1002/(sici)1098-111x(199705)12:5<391::aid-int3>3.0.co;2-i.

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9

Bashir, Shariq, and Daphne Teck Ching Lai. "Mining Approximate Frequent Itemsets Using Pattern Growth Approach." Information Technology and Control 50, no. 4 (December 16, 2021): 627–44. http://dx.doi.org/10.5755/j01.itc.50.4.29060.

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Approximate frequent itemsets (AFI) mining from noisy databases are computationally more expensive than traditional frequent itemset mining. This is because the AFI mining algorithms generate large number of candidate itemsets. This article proposes an algorithm to mine AFIs using pattern growth approach. The major contribution of the proposed approach is it mines core patterns and examines approximate conditions of candidate AFIs directly with single phase and two full scans of database. Related algorithms apply Apriori-based candidate generation and test approach and require multiple phases to obtain complete AFIs. First phase generates core patterns, and second phase examines approximate conditions of core patterns. Specifically, the article proposes novel techniques that how to map transactions on approximate FP-tree, and how to mine AFIs from the conditional patterns of approximate FP-tree. The approximate FP-tree maps transactions on shared branches when the transactions share a similar set of items. This reduces the size of databases and helps to efficiently compute the approximate conditions of candidate itemsets. We compare the performance of our algorithm with the state of the art AFI mining algorithms on benchmark databases. The experiments are analyzed by comparing the processing time of algorithms and scalability of algorithms on varying database size and transaction length. The results show pattern growth approach mines AFIs in less processing time than related Apriori-based algorithms.

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10

CHEN, Siyu, Ning WANG, and Mengmeng ZHANG. "Mining Approximate Primary Functional Dependency on Web Tables." IEICE Transactions on Information and Systems E102.D, no. 3 (March 1, 2019): 650–54. http://dx.doi.org/10.1587/transinf.2018edl8130.

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11

Superfesky, Michael J. "ACHIEVING APPROXIMATE ORIGINAL CONTOUR IN MOUNTAIN TOP MINING." Journal American Society of Mining and Reclamation 2000, no. 1 (2000): 487–92. http://dx.doi.org/10.21000/jasmr00010487.

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12

Yun, Unil, Keun Ho Ryu, and Eunchul Yoon. "Weighted approximate sequential pattern mining within tolerance factors." Intelligent Data Analysis 15, no. 4 (June 23, 2011): 551–69. http://dx.doi.org/10.3233/ida-2011-0482.

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13

Baek, Yoonji, Unil Yun, Heonho Kim, Jongseong Kim, Bay Vo, Tin Truong, and Zhi-Hong Deng. "Approximate high utility itemset mining in noisy environments." Knowledge-Based Systems 212 (January 2021): 106596. http://dx.doi.org/10.1016/j.knosys.2020.106596.

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14

Gupta, Parul, Swati Agnihotri, and Suman Saha. "Approximate Data Mining Using Sketches for Massive Data." Procedia Technology 10 (2013): 781–87. http://dx.doi.org/10.1016/j.protcy.2013.12.422.

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15

Uppal, Veepu. "An Efficient Algorithm for Approximate Frequent Intemset Mining." International Journal of Database Theory and Application 8, no. 3 (June 30, 2015): 279–88. http://dx.doi.org/10.14257/ijdta.2015.8.3.24.

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16

Nakamura, Atsuyoshi, Ichigaku Takigawa, Hisashi Tosaka, Mineichi Kudo, and Hiroshi Mamitsuka. "Mining approximate patterns with frequent locally optimal occurrences." Discrete Applied Mathematics 200 (February 2016): 123–52. http://dx.doi.org/10.1016/j.dam.2015.07.002.

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17

Tang, Huijun, Le Wang, Yangguang Liu, and Jiangbo Qian. "Discovering Approximate and Significant High-Utility Patterns from Transactional Datasets." Journal of Mathematics 2022 (November 16, 2022): 1–17. http://dx.doi.org/10.1155/2022/6975130.

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Mining high-utility pattern (HUP) on transactional datasets has been widely discussed, and various algorithms have been introduced to settle this problem. However, the time-space efficiency of the algorithms is still limited, and the mining system cannot provide timely feedback on relevant information. In addition, when mining HUP from taxonomy transactional datasets, a large portion of the quantitative results are just accidental responses to the user-defined utility constraints, and they may have no statistical significance. To address these two problems, we propose two corresponding approaches named Sampling HUP-Miner and Significant HUP-Miner. Sampling HUP-Miner pursues a sample size of a transitional dataset based on a theoretical guarantee; the mining results based on such a sample size can be an effective approximation to the results on the whole datasets. Significant HUP-Miner proposes the concept of testable support, and significant HUPs could be drawn timely based on the constraint of testable support. Experiments show that the designed two algorithms can discover approximate and significant HUPs smoothly and perform well according to the runtime, pattern numbers, memory usage, and average utility.

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18

Hadi, Raghad M. "Best Approximate of Vector Space Model by Using SVD." Al-Mustansiriyah Journal of Science 28, no. 2 (April 11, 2018): 143. http://dx.doi.org/10.23851/mjs.v28i2.509.

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Анотація:

A quick growth of internet technology makes it easy to assemble a huge volume of data as text document; e. g., journals, blogs, network pages, articles, email letters. In text mining application, increasing text space of datasets represent excessive task which makes it hard to pre-processing documents in efficient way to prepare it for text mining application like document clustering. The proposed system focuses on pre-processing document and reduction document space technique to prepare it for clustering technique. The mutual method for text mining problematic is vector space model (VSM), each term represent a features. Thus the proposed system create vector-space mod-el by using pre-processing method to reduce of trivial data from dataset. While the hug dimen-sionality of VSM is resolved by using low-rank SVD. Experiment results show that the proposed system give better document representation results about 10% from previous approach to prepare it for document clustering

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19

Nasir, Muhammad Anis Uddin, Cigdem Aslay, Gianmarco De Francisci Morales, and Matteo Riondato. "Approximate Mining of Frequent -Subgraph Patterns in Evolving Graphs." ACM Transactions on Knowledge Discovery from Data 15, no. 3 (April 12, 2021): 1–35. http://dx.doi.org/10.1145/3442590.

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“Perhaps he could dance first and think afterwards, if it isn’t too much to ask him.” S. Beckett, Waiting for Godot Given a labeled graph, the collection of -vertex induced connected subgraph patterns that appear in the graph more frequently than a user-specified minimum threshold provides a compact summary of the characteristics of the graph, and finds applications ranging from biology to network science. However, finding these patterns is challenging, even more so for dynamic graphs that evolve over time, due to the streaming nature of the input and the exponential time complexity of the problem. We study this task in both incremental and fully-dynamic streaming settings, where arbitrary edges can be added or removed from the graph. We present TipTap , a suite of algorithms to compute high-quality approximations of the frequent -vertex subgraphs w.r.t. a given threshold, at any time (i.e., point of the stream), with high probability. In contrast to existing state-of-the-art solutions that require iterating over the entire set of subgraphs in the vicinity of the updated edge, TipTap operates by efficiently maintaining a uniform sample of connected -vertex subgraphs, thanks to an optimized neighborhood-exploration procedure. We provide a theoretical analysis of the proposed algorithms in terms of their unbiasedness and of the sample size needed to obtain a desired approximation quality. Our analysis relies on sample-complexity bounds that use Vapnik–Chervonenkis dimension, a key concept from statistical learning theory, which allows us to derive a sufficient sample size that is independent from the size of the graph. The results of our empirical evaluation demonstrates that TipTap returns high-quality results more efficiently and accurately than existing baselines.

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20

Jiadong Ren, Yufeng Tian, Haitao He, Xiao Cui, and Qian Wang. "Mining approximate Time-interval sequential pattern in data stream." Journal of Convergence Information Technology 7, no. 3 (February 29, 2012): 282–91. http://dx.doi.org/10.4156/jcit.vol7.issue3.33.

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21

Kum, Hye-Chung, and Joong-Hyuk Chang. "Mining Approximate Sequential Patterns in a Large Sequence Database." KIPS Transactions:PartD 13D, no. 2 (April 1, 2006): 199–206. http://dx.doi.org/10.3745/kipstd.2006.13d.2.199.

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22

Liao, Zhifang, Limin Liu, Xiaoping Fan, Yueshan Xie, Zhining Liao, and Yan Zhang. "An outlier mining algorithm based on approximate outlier factor." International Journal of Autonomous and Adaptive Communications Systems 8, no. 2/3 (2015): 243. http://dx.doi.org/10.1504/ijaacs.2015.069567.

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23

Chaudhuri, Surajit, Venkatesh Ganti, and Dong Xin. "Mining document collections to facilitate accurate approximate entity matching." Proceedings of the VLDB Endowment 2, no. 1 (August 2009): 395–406. http://dx.doi.org/10.14778/1687627.1687673.

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24

Li, Haifeng, Yuejin Zhang, Ning Zhang, and Hengyue Jia. "A Heuristic Rule Based Approximate Frequent Itemset Mining Algorithm." Procedia Computer Science 91 (2016): 324–33. http://dx.doi.org/10.1016/j.procs.2016.07.087.

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25

Yun, Unil, and Keun Ho Ryu. "Approximate weighted frequent pattern mining with/without noisy environments." Knowledge-Based Systems 24, no. 1 (February 2011): 73–82. http://dx.doi.org/10.1016/j.knosys.2010.07.007.

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26

Liu, Yijun, Feiyue Ye, Jixue Liu, and Sheng He. "Mining Approximate Keys based on Reasoning from XML Data." Applied Mathematics & Information Sciences 8, no. 4 (July 1, 2014): 2005–16. http://dx.doi.org/10.12785/amis/080459.

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27

Liu, Shengxin, and Chung Keung Poon. "On mining approximate and exact fault-tolerant frequent itemsets." Knowledge and Information Systems 55, no. 2 (July 11, 2017): 361–91. http://dx.doi.org/10.1007/s10115-017-1079-4.

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28

Acosta-Mendoza, Niusvel, Jesús Ariel Carrasco-Ochoa, José Francisco Martínez-Trinidad, Andrés Gago-Alonso, and José Eladio Medina-Pagola. "Mining clique frequent approximate subgraphs from multi-graph collections." Applied Intelligence 50, no. 3 (October 19, 2019): 878–92. http://dx.doi.org/10.1007/s10489-019-01564-8.

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29

Yun, Unil, and Eunchul Yoon. "An Efficient Approach for Mining Weighted Approximate Closed Frequent Patterns Considering Noise Constraints." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 22, no. 06 (December 2014): 879–912. http://dx.doi.org/10.1142/s0218488514500470.

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Анотація:

Based on the frequent pattern mining, closed frequent pattern mining and weighted frequent pattern mining have been studied to reduce the search space and discover important patterns. In the previous definition of weighted closed patterns, supports of patterns are only considered to compute the closures of the patterns. It means that the closures of weighted frequent patterns cannot be perfectly checked. Moreover, the usefulness of weighted closed frequent patterns depends on the presence of frequent patterns that have supersets with the exactly same weighted support. However, from the errors such as noise, slight changes in items' supports or weights by them have significantly negative effects on the mining results, which may prevent us from obtaining exact and valid analysis results since the errors can break the original characteristics of items and patterns. In this paper, to solve the above problems, we propose a concept of robust weighted closed frequent pattern mining, and an approximate bound is defined on the basis of the concept, which can relax requirements for precise equality among patterns' weighted supports. Thereafter, we propose a weighted approximate closed frequent pattern mining algorithm which not only considers the two approaches but also suggests fault tolerant pattern mining in the noise constraints. To efficiently mine weighted approximate closed frequent patterns, we suggest pruning and subset checking methods which reduce search space. We also report extensive performance study to demonstrate the effectiveness, efficiency, memory usage, scalability, and quality of patterns in our algorithm.

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30

Acosta-Mendoza, Niusvel, Andrés Gago-Alonso, Jesús Ariel Carrasco-Ochoa, José Fco Martínez-Trinidad, and José E. Medina-Pagola. "Extension of Canonical Adjacency Matrices for Frequent Approximate Subgraph Mining on Multi-Graph Collections." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 08 (May 9, 2017): 1750025. http://dx.doi.org/10.1142/s0218001417500252.

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Into the data mining field, frequent approximate subgraph (FAS) mining has become an important technique with a broad spectrum of real-life applications. This fact is because several real-life phenomena can be modeled by graphs. In the literature, several algorithms have been reported for mining frequent approximate patterns on simple-graph collections; however, there are applications where more complex data structures, as multi-graphs, are needed for modeling the problem. But to the best of our knowledge, there is no FAS mining algorithm designed for dealing with multi-graphs. Therefore, in this paper, a canonical form (CF) for simple-graphs is extended to allow representing multi-graphs and a state-of-the-art algorithm for FAS mining is also extended for processing multi-graph collections by using the extended CF. Our experiments over different synthetic and real-world multi-graph collections show that the proposed algorithm has a good performance in terms of runtime and scalability. Additionally, we show the usefulness of the patterns computed by our algorithm in an image classification problem where images are represented as multi-graphs.

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31

Valiullin, Timur, Zhexue Huang, Chenghao Wei, Jianfei Yin, Dingming Wu, and Luliia Egorova. "A new approximate method for mining frequent itemsets from big data." Computer Science and Information Systems, no. 00 (2020): 15. http://dx.doi.org/10.2298/csis200124015v.

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Mining frequent itemsets in transaction databases is an important task in many applications. It becomes more challenging when dealing with a large transaction database because traditional algorithms are not scalable due to the memory limit. In this paper, we propose a new approach for approximately mining of frequent itemsets in a big transaction database. Our approach is suitable for mining big transaction databases since it produces approximate frequent itemsets from a subset of the entire database, and can be implemented in a distributed environment. Our algorithm is able to efficiently produce high-accurate results, however it misses some true frequent itemsets. To address this problem and reduce the number of false negative frequent itemsets we introduce an additional parameter to the algorithm to discover most of the frequent itemsets contained in the entire data set. In this article, we show an empirical evaluation of the results of the proposed approach.

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32

HUANG, Chong-Zheng, Hai-Feng LI, and Hong CHEN. "An Approximate Non-Derivable Itemset Mining Algorithm over Data Streams." Chinese Journal of Computers 33, no. 8 (December 1, 2010): 1427–36. http://dx.doi.org/10.3724/sp.j.1016.2010.01427.

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33

Atoum, Jalal. "Approximate Functional Dependencies Mining Using Association Rules Specificity Interestingness Measure." British Journal of Mathematics & Computer Science 15, no. 5 (January 10, 2016): 1–10. http://dx.doi.org/10.9734/bjmcs/2016/25206.

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34

Lucchese, Claudio, Salvatore Orlando, and Raffaele Perego. "A Unifying Framework for Mining Approximate Top- $k$ Binary Patterns." IEEE Transactions on Knowledge and Data Engineering 26, no. 12 (December 2014): 2900–2913. http://dx.doi.org/10.1109/tkde.2013.181.

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35

Guo, Lichao, Hongye Su, and Yu Qu. "Approximate mining of global closed frequent itemsets over data streams." Journal of the Franklin Institute 348, no. 6 (August 2011): 1052–81. http://dx.doi.org/10.1016/j.jfranklin.2011.04.006.

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36

Yu, Xiaomei, Hong Wang, and Xiangwei Zheng. "Mining top-k approximate closed patterns in an imprecise database." International Journal of Grid and Utility Computing 9, no. 2 (2018): 97. http://dx.doi.org/10.1504/ijguc.2018.091696.

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37

Zheng, Xiangwei, Xiaomei Yu, and Hong Wang. "Mining top-k approximate closed patterns in an imprecise database." International Journal of Grid and Utility Computing 9, no. 2 (2018): 97. http://dx.doi.org/10.1504/ijguc.2018.10012791.

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38

He, Dan, Xingquan Zhu, and Xindong Wu. "MINING APPROXIMATE REPEATING PATTERNS FROM SEQUENCE DATA WITH GAP CONSTRAINTS." Computational Intelligence 27, no. 3 (August 2011): 336–62. http://dx.doi.org/10.1111/j.1467-8640.2011.00383.x.

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39

Wu, Youxi, Bojing Jian, Yan Li, He Jiang, and Xindong Wu. "NetNDP: Nonoverlapping (delta, gamma)-approximate pattern matching." Intelligent Data Analysis 26, no. 6 (November 12, 2022): 1661–82. http://dx.doi.org/10.3233/ida-216325.

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Анотація:

Pattern matching can be used to calculate the support of patterns, and is a key issue in sequential pattern mining (or sequence pattern mining). Nonoverlapping pattern matching means that two occurrences cannot use the same character in the sequence at the same position. Approximate pattern matching allows for some data noise, and is more general than exact pattern matching. At present, nonoverlapping approximate pattern matching is based on Hamming distance, which cannot be used to measure the local approximation between the subsequence and pattern, resulting in large deviations in matching results. To tackle this issue, we present a Nonoverlapping Delta and gamma approximate Pattern matching (NDP) scheme that employs the (δ,γ)-distance to give an approximate pattern matching, where the local and the global distances do not exceed δ and γ, respectively. We first transform the NDP problem into a local approximate Nettree and then construct an efficient algorithm, called the local approximate Nettree for NDP (NetNDP). We propose a new approach called the Minimal Root Distance which allows us to determine whether or not a node has root paths that satisfy the global constraint and to prune invalid nodes and parent-child relationships. NetNDP finds the rightmost absolute leaf of the max root, searches for the rightmost occurrence from the rightmost absolute leaf, and deletes this occurrence. We iterate the above steps until there are no new occurrences. Numerous experiments are used to verify the performance of the proposed algorithm.

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40

Chang, Chia-Yo, Jason T. L. Wang, and Roger K. Chang. "Scientific Data Mining: A Case Study." International Journal of Software Engineering and Knowledge Engineering 08, no. 01 (March 1998): 77–96. http://dx.doi.org/10.1142/s0218194098000078.

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Scientific data mining is the activity of finding significant information in scientific data. This paper presents an example of scientific data mining: the discovery of approximately common patterns in RNA secondary structures. We represent an RNA secondary structure by an ordered labeled tree based on a previously proposed scheme. The patterns in the trees are substructures that can differ in both substitutions and deletions/insertions of nodes of the trees. Our techniques incorporate approximate tree matching algorithms and novel heuristics for discovery and optimization. Experimental results obtained by running these algorithms on both generated data and RNA secondary structures show the good performance of the algorithms. It is shown that the optimization heuristics speed up the discovery algorithm by a factor of 10. Moreover, our optimized approach is 100,000 times faster than the brute force method.

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41

Kuzniar, Krystyna, Krystyna Stec, and Tadeusz Tatara. "Approximate classification of mining tremors harmfulness based on free-field and building foundation vibrations." E3S Web of Conferences 36 (2018): 01006. http://dx.doi.org/10.1051/e3sconf/20183601006.

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The paper compares the results of an approximate evaluation of mining tremors harmfulness performed on the basis of free-field and simultaneously measured building foundation vibrations. The focus is on the office building located in the Upper Silesian Basin (USB). The empirical Mining Intensity Scale GSI-GZWKW-2012 has been applied to classify the harmfulness of the rockbursts. This scale is based on the measurements of free-field vibrations but, for research purposes, it was also used in the cases of building foundation vibrations. The analysis was carried out using the set of 156 pairs ground – foundation of velocity vibration records as well as the set of 156 pairs of acceleration records induced by the same mining tremors.

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Jiadong Ren, Yufeng Tian, and Haitao He. "Bitmap-based Algorithm of Mining Approximate Sequential Pattern in Data Stream." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 3, no. 9 (October 31, 2011): 132–39. http://dx.doi.org/10.4156/aiss.vol3.issue9.18.

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Pyun, Gwangbum, and Unil Yun. "Performance evaluation of approximate frequent pattern mining based on probabilistic technique." Journal of Korean Society for Internet Information 14, no. 1 (February 28, 2013): 63–69. http://dx.doi.org/10.7472/jksii.2013.14.63.

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WANG, Wei-Ping. "An Efficient Algorithm for Mining Approximate Frequent Item over Data Streams." Journal of Software 18, no. 4 (2007): 884. http://dx.doi.org/10.1360/jos180884.

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45

Lee, Gangin, Unil Yun, Heungmo Ryang, and Donggyu Kim. "Approximate Maximal Frequent Pattern Mining with Weight Conditions and Error Tolerance." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 06 (May 9, 2016): 1650012. http://dx.doi.org/10.1142/s0218001416500129.

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Since the concept of frequent pattern mining was proposed, there have been many efforts to obtain useful pattern information from large databases. As one of them, applying weight conditions allows us to mine weighted frequent patterns considering unique importance of each item composing databases, and the result of analysis for the patterns provides more useful information than that of considering only frequency or support information. However, although this approach gives us more meaningful pattern information, the number of patterns found from large databases is extremely large in general; therefore, analyzing all of them may become inefficient and hard work. Thus, it is essential to apply a method that can selectively extract representative patterns from the enormous ones. Moreover, in the real-world applications, unexpected errors such as noise may occur, which can have a negative effect on the values of databases. Although the changes by the error are quite small, the characteristics of generated patterns can be turned definitely. For this reason, we propose a novel algorithm that can solve the above problems, called AWMax (an algorithm for mining Approximate weighted maximal frequent patterns (AWMFPs) considering error tolerance). Through the algorithm, we can obtain useful AWMFPs regardless of noise because of the consideration of error tolerance. Comprehensive performance experiments present that the proposed algorithm has more outstanding performance than previous state-of-the-art ones.

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46

Liu, Haibin, Lawrence Hunter, Vlado Kešelj, and Karin Verspoor. "Approximate Subgraph Matching-Based Literature Mining for Biomedical Events and Relations." PLoS ONE 8, no. 4 (April 17, 2013): e60954. http://dx.doi.org/10.1371/journal.pone.0060954.

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Acosta-Mendoza, Niusvel, Andrés Gago-Alonso, Jesús Ariel Carrasco-Ochoa, José Francisco Martínez-Trinidad, and José Eladio Medina-Pagola. "A new algorithm for approximate pattern mining in multi-graph collections." Knowledge-Based Systems 109 (October 2016): 198–207. http://dx.doi.org/10.1016/j.knosys.2016.07.003.

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Yu, Xiaomei, Jun Zhao, Hong Wang, Xiangwei Zheng, and Xiaoyan Yan. "A model of mining approximate frequent itemsets using rough set theory." International Journal of Computational Science and Engineering 19, no. 1 (2019): 71. http://dx.doi.org/10.1504/ijcse.2019.099640.

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Yan, Xiaoyan, Jun Zhao, Hong Wang, Xiangwei Zheng, and Xiaomei Yu. "A model of mining approximate frequent itemsets using rough set theory." International Journal of Computational Science and Engineering 19, no. 1 (2019): 71. http://dx.doi.org/10.1504/ijcse.2019.10020958.

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Aridhi, Sabeur, Laurent d'Orazio, Mondher Maddouri, and Engelbert Mephu Nguifo. "Density-based data partitioning strategy to approximate large-scale subgraph mining." Information Systems 48 (March 2015): 213–23. http://dx.doi.org/10.1016/j.is.2013.08.005.

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