Journal articles: 'Frequent pattern analysis'

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Giri, Ritesh, Ananta Bhatt, and Aadhya Bhatt. "Frequent Pattern Mining Algorithms Analysis." International Journal of Computer Applications 145, no. 9 (July 15, 2016): 33–36. http://dx.doi.org/10.5120/ijca2016910763.

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Stefanowitsch, Anatol. "Paradigmatic pattern analysis." Yearbook of the German Cognitive Linguistics Association 8, no. 1 (October 27, 2020): 119–40. http://dx.doi.org/10.1515/gcla-2020-0008.

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AbstractThe phrase Sprache ist der Schlüssel zur Integration (“language is the key to integration”) is found frequently in the discourse around immigration in the German-speaking countries. Based on a corpus-linguistic analysis of this phrase, this paper proposes the existence of a particular type of constructional idiom I refer to as ‘paradigmatic pattern’. Like a metaphorical pattern (in the sense of Metaphorical Pattern Analysis), a paradigmatic pattern establishes a correspondence between a word occurring in a particular slot of the idiom and another word more typical of that slot, contributing to a conceptual mapping between the domains instantiated by these words. Unlike in the case of metaphorical patterns, the domain evoked by the paradigmatic pattern is not the domain in which the pattern occurs in its literal meaning, but a domain evoked by a highly frequent collexeme in one of the slots. As in the case of metaphorical patterns, however, this collexeme contributes (aspects of) its meaning even when it is replaced by another word. I suggest a generalized version of Metaphorical Pattern Analysis, referred to as Paradigmatic Pattern Analysis, to deal with such expressions.

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Umar, Aqsa, Naeem Ahemd Mahoto, Sania Bhatti, and Sapna Rathi. "Analysis of Covid-19 Genome Sequences based on Geo-Locations." Pakistan Journal of Engineering and Technology 4, no. 4 (December 22, 2021): 41–45. http://dx.doi.org/10.51846/vol4iss4pp41-45.

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The COVID-19 pandemic has become a major worldwide serious health risk of the current 21st century. It is necessary to examine the genomic sequences of the deadly virus COVID-19 strains to fully understand the virus’s behavior, origin, and how rapidly it mutates. This paper addresses the analysis of the COVID-19 genome sequences CGS of China, Pakistan, and India. In this research, we have looked at the usage of sequential pattern mining (SPM), a closed sequential pattern technique to discover valuable information from COVID-19 genomic sequences. The analysis is performed on the three strains of genome sequences. First, the sequences data files of genome sequences are being transformed to the computer-readable corpus of CGS and then the SPM technique is applied to discover the frequent patterns of nucleotides. Second, Frequent codons of Amino acids are extracted from three strains of genome sequences. Third, we have evaluated the performance of the proposed approach in terms of time execution, the number of frequent patterns, and memory consumption. Obtained results suggest that the codon of Threonine amino acid ACA with support 1576 in Pakistan is the most frequent pattern from the other two strains of CGS. Furthermore, when the user minimum threshold value is low, the closed sequential pattern mining using sparse and vertical id-lists CloFAST algorithm performance evaluates that a high number of frequent patterns consumes more time and memory

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Vyas, Heli. "A Comparative Analysis of Frequent Pattern Mining Algorithms." International Journal for Research in Applied Science and Engineering Technology V, no. XI (November 23, 2017): 3010–12. http://dx.doi.org/10.22214/ijraset.2017.11415.

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Ożdżyński, Piotr. "USING FREQUENT PATTERN MINING ALGORITHMS IN TEXT ANALYSIS." Information System in Management 6, no. 3 (September 30, 2017): 213–22. http://dx.doi.org/10.22630/isim.2017.6.3.19.

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Ożdżyński, Piotr. "USING FREQUENT PATTERN MINING ALGORITHMS IN TEXT ANALYSIS." Information System in Management 6, no. 3 (September 30, 2017): 213–22. http://dx.doi.org/10.22630/isim.2017.6.3.5.

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Shou, Zhenyu, and Xuan Di. "Similarity analysis of frequent sequential activity pattern mining." Transportation Research Part C: Emerging Technologies 96 (November 2018): 122–43. http://dx.doi.org/10.1016/j.trc.2018.09.018.

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Santoro, Diego, Andrea Tonon, and Fabio Vandin. "Mining Sequential Patterns with VC-Dimension and Rademacher Complexity." Algorithms 13, no. 5 (May 18, 2020): 123. http://dx.doi.org/10.3390/a13050123.

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Sequential pattern mining is a fundamental data mining task with application in several domains. We study two variants of this task—the first is the extraction of frequent sequential patterns, whose frequency in a dataset of sequential transactions is higher than a user-provided threshold; the second is the mining of true frequent sequential patterns, which appear with probability above a user-defined threshold in transactions drawn from the generative process underlying the data. We present the first sampling-based algorithm to mine, with high confidence, a rigorous approximation of the frequent sequential patterns from massive datasets. We also present the first algorithms to mine approximations of the true frequent sequential patterns with rigorous guarantees on the quality of the output. Our algorithms are based on novel applications of Vapnik-Chervonenkis dimension and Rademacher complexity, advanced tools from statistical learning theory, to sequential pattern mining. Our extensive experimental evaluation shows that our algorithms provide high-quality approximations for both problems we consider.

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Lee, Gangin, Unil Yun, and Kyung-Min Lee. "Analysis of tree-based uncertain frequent pattern mining techniques without pattern losses." Journal of Supercomputing 72, no. 11 (August 19, 2016): 4296–318. http://dx.doi.org/10.1007/s11227-016-1847-z.

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Ahmad, Munir, Umar Farooq, Atta-Ur-Rahman, Abdulrahman Alqatari, Sujata Dash, and Ashish Kr Luhach. "Investigating TYPE constraint for frequent pattern mining." Journal of Discrete Mathematical Sciences and Cryptography 22, no. 4 (May 19, 2019): 605–26. http://dx.doi.org/10.1080/09720529.2019.1637158.

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Lu, Ping-Hsun, Jui-Lin Keng, Fu-Ming Tsai, Po-Hsuan Lu, and Chan-Yen Kuo. "An Apriori Algorithm-Based Association Rule Analysis to Identify Acupoint Combinations for Treating Diabetic Gastroparesis." Evidence-Based Complementary and Alternative Medicine 2021 (March 25, 2021): 1–9. http://dx.doi.org/10.1155/2021/6649331.

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We explored the potential association rules within acupoints in treating diabetic gastroparesis (DGP) using Apriori algorithm complemented with another partition-based algorithm, a frequent pattern growth algorithm. Apriori algorithm is a data mining-based analysis that is widely applied in various fields, such as business and medicine, to mine frequent patterns in datasets. To search for effective acupoint combinations in the treatment of DGP, we implemented Apriori algorithm to investigate the association rules of acupoints among 17 randomized controlled trials (RCTs). The acupoints were extracted from the 17 included RCTs. In total, 29 distinct acupoints were observed in the RCTs. The top 10 frequently selected acupoints were CV12, ST36, PC6, ST25, BL21, BL20, BL23, SP6, BL18, and ST21. The frequency pattern of acupoints achieved by using a frequent pattern growth algorithm also confirms the result. The results showed that the most associated rules were {BL23, BL18} ≥ {SP6}, {BL20, BL18} ≥ {PC6}, {PC6, BL18} ≥ {BL20}, and {SP6, BL18} ≥ {BL23} in the database. Acupoints, including BL23, BL18, SP6, BL20, and PC6, can be deemed as core elements of acupoint combinations for treating DGP.

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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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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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Deeba, M., and Mary Immaculate Sheela. "Text Mining Using Frequent Pattern Analysis and Message Passing." International Journal of Computer Sciences and Engineering 7, no. 2 (February 28, 2019): 658–67. http://dx.doi.org/10.26438/ijcse/v7i2.658667.

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Zuo, Zhonghu, Chunhong Zhang, Xiaosheng Tang, Zheng Hu, and Yuqian Tang. "Frequent Subgraph Mining Based Collaboration Pattern Analysis for Wikipedia." Information Technology And Control 48, no. 2 (June 25, 2019): 195–210. http://dx.doi.org/10.5755/j01.itc.48.2.20028.

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Online knowledge collaborations, where distributed members without hierarchies self-organize themselvesto create valuable contents, are prevalent in many open production systems such as Wikipedia, GitHub andsocial networks. While many existing studies from network science have been brought to analyse the general interactivebehavioural patterns embedded in these systems, how the collaborations influence the achievement outcomes hasnot been thoroughly investigated. In this paper, we mine the collaboration patterns from a micro perspective to deeplyunderstand the relationships between the collaboration among participants and the qualities of theWikipedia articles.In particular, the subgraphs contained in the collaboration networks derived from theWikipedia revision histories aretaken as the fundamental units to analyse the collaboration diversities from the subgraph properties such as size andtopology. In contrast to the predefined static motifs adopted by the previous works, the collaboration subgraphs aredirectly found from Wikipedia dataset by a frequent subgraph mining algorithm GRAMI, which is able to capturethe real dynamic collaboration patterns. Moreover, the relationships between the co-authors in the subgraphs are alsodiscriminated to further explore the collaboration patterns. The experiments exhibit the statistical properties of thecollaboration subgraphs and the efficiency of them as the metrics for the article quality assessments. We concludethat a small group of editors with relative frequent fixed collaboration patterns contribute more to the excellent articlequality than the professional extents of arbitrary individuals in the collaboration group. This discovery confirms thecommonly insight about collaboration that many heads are always better than one and concretely suggests a potentialexplanation for the increasing prevalence and success of the online knowledge collaborations

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Song, Yuanfeng, Wilfred Ng, Kenneth Wai-Ting Leung, and Qiong Fang. "SFP-Rank: significant frequent pattern analysis for effective ranking." Knowledge and Information Systems 43, no. 3 (March 25, 2014): 529–53. http://dx.doi.org/10.1007/s10115-014-0738-y.

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Madan Kumar, K. M. V., and B. Srinivasa Rao. "Mining Frequent Utility Sequential Patterns in Progressive Databases by U-Pisa." Journal of Computational and Theoretical Nanoscience 17, no. 4 (April 1, 2020): 1786–95. http://dx.doi.org/10.1166/jctn.2020.8442.

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Sequential pattern mining is one of the most important aspects of data mining world and has a significant role in many applications like market analysis, biomedical analysis, weather forecasting etc. In the category of mining sequential patterns the usage of progressive database as an input database is relatively new and has a wide impact in decision-making system. In progressive sequential pattern mining, we discover the frequent sequences progressively with the help of period of Interest. As the traditional approaches of frequency based framework are not much more informative for decision making, in recent effort utility framework has been incorporated instead of frequency. This addressed many typical business concerns such as profit value associated with each pattern. In this paper, we applied the concept of frequent utility over the progressive database and discovered the sequential pattern efficiently. To do so we proposed an algorithm called U-Pisa which works progressively with the help of a quantitative progressive database. We conducted sub-stantial experiments on the proposed algorithm and proved that this process performs well.

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Bok, Kyoungsoo, Jaeyun Jeong, Dojin Choi, and Jaesoo Yoo. "Detecting Incremental Frequent Subgraph Patterns in IoT Environments." Sensors 18, no. 11 (November 18, 2018): 4020. http://dx.doi.org/10.3390/s18114020.

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As graph stream data are continuously generated in Internet of Things (IoT) environments, many studies on the detection and analysis of changes in graphs have been conducted. In this paper, we propose a method that incrementally detects frequent subgraph patterns by using frequent subgraph pattern information generated in previous sliding window. To reduce the computation cost for subgraph patterns that occur consecutively in a graph stream, the proposed method determines whether subgraph patterns occur within a sliding window. In addition, subgraph patterns that are more meaningful can be detected by recognizing only the patterns that are connected to each other via edges as one pattern. In order to prove the superiority of the proposed method, various performance evaluations were conducted.

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Xia, Dawen, Xiaonan Lu, Huaqing Li, Wendong Wang, Yantao Li, and Zili Zhang. "A MapReduce-Based Parallel Frequent Pattern Growth Algorithm for Spatiotemporal Association Analysis of Mobile Trajectory Big Data." Complexity 2018 (2018): 1–16. http://dx.doi.org/10.1155/2018/2818251.

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Frequent pattern mining is an effective approach for spatiotemporal association analysis of mobile trajectory big data in data-driven intelligent transportation systems. While existing parallel algorithms have been successfully applied to frequent pattern mining of large-scale trajectory data, two major challenges are how to overcome the inherent defects of Hadoop to cope with taxi trajectory big data including massive small files and how to discover the implicitly spatiotemporal frequent patterns with MapReduce. To conquer these challenges, this paper presents a MapReduce-based Parallel Frequent Pattern growth (MR-PFP) algorithm to analyze the spatiotemporal characteristics of taxi operating using large-scale taxi trajectories with massive small file processing strategies on a Hadoop platform. More specifically, we first implement three methods, that is, Hadoop Archives (HAR), CombineFileInputFormat (CFIF), and Sequence Files (SF), to overcome the existing defects of Hadoop and then propose two strategies based on their performance evaluations. Next, we incorporate SF into Frequent Pattern growth (FP-growth) algorithm and then implement the optimized FP-growth algorithm on a MapReduce framework. Finally, we analyze the characteristics of taxi operating in both spatial and temporal dimensions by MR-PFP in parallel. The results demonstrate that MR-PFP is superior to existing Parallel FP-growth (PFP) algorithm in efficiency and scalability.

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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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Ryang, Heungmo, and Unil Yun. "Performance Analysis of Frequent Pattern Mining with Multiple Minimum Supports." Journal of Korean Society for Internet Information 14, no. 6 (December 31, 2013): 1–8. http://dx.doi.org/10.7472/jksii.2013.14.6.01.

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Sakaba, Hiroko, and Takeshi Okada. "Usage Patterns and Meanings of High-Frequency English Verbs: A Multi-Word Expression Approach to Japanese High School EFL Textbook Analysis." International Journal of Applied Linguistics and English Literature 10, no. 4 (July 31, 2021): 116. http://dx.doi.org/10.7575/aiac.ijalel.v.10n.4p.116.

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This article aims to classify the overall uses of high-frequency English verbs in a novel methodology from both a pattern and meaning perspective, which has not be done in previous studies, with special reference to TAKE and MAKE. In the pattern-based analysis, all occurrences of these two verbs were collected from Japanese EFL textbook corpus, and the usage patterns of the extracted two target verbs were categorized into three major multi-word expression types: phrasal verbs, grammatical collocations, and lexical collocations. To further investigate the patterns of uses, some multi-word units consisting of three to seven words were identified as either semi-fixed expressions or fixed expressions. After the pattern-based classification, all the multi-word expressions identified were analyzed from a semantic perspective. This analysis revealed the new finding that all uses of TAKE (352) and MAKE (374) obtained from the corpus could be successfully classified into the three major multi-word expression categories. With respect to the pattern, the proportion of major multi-word expression categories showed similar results; lexical collocations were the most frequent, and phrasal verbs were the least frequent in both target verbs’ usage. In terms of meanings, the uses of TAKE were classified in a larger number of semantic categories (42) than MAKE (25). The obtained results have an implication that the novel methodology employed in this study is a valid way to the further investigation of the usage of high-frequency English verbs.

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Markovic, Slobodan, and Vasilije Gvozdenovic. "Microgenetic analysis of hidden figures." Psihologija 39, no. 1 (2006): 5–20. http://dx.doi.org/10.2298/psi0601005m.

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In this study the phenomenological and processual aspects of the perception of hidden figures were compared. The question was whether the more probable percepts of hidden figures, compared to the less probable percepts, were generated in earlier stages of the perceptual process. In the pilot study the subjects were asked to say what they see in a complex linear pattern. The three most frequent and the three least frequent perceptual descriptions were selected. In the experiment the microgenesis of the perception of hidden figures was investigated. The primed matching paradigm and the same-different task were used. In each experiment two types of test figures were contrasted: the more frequent and the less frequent ones. There were two prime types: identical (equal to test figures) and complex (the pattern with hidden test figures). The prime duration was varied, 50 ms and 400 ms. The main result indicates that in the case of complex priming the more frequent test figures were processed significantly faster than the less frequent ones in both prime duration conditions. These results suggest that the faster the processing of a figure, the more probable the perceptual generation of this figure.

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Kjøllesdal, Marte Råberg, Gerd Holmboe-Ottesen, and Margareta Wandel. "Frequent use of staff canteens is associated with unhealthy dietary habits and obesity in a Norwegian adult population." Public Health Nutrition 14, no. 1 (June 8, 2010): 133–41. http://dx.doi.org/10.1017/s1368980010001473.

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AbstractObjectiveTo explore socio-economic differences in use of staff canteens and whether frequent use of staff canteens is associated with different food patterns and obesity.DesignCross-sectional study using three self-administered questionnaires, two of them including food frequency questions. Factor analysis was used to explore food patterns.SettingOslo, Norway, 2000–2001.SubjectsIn total 8943 adult, working Oslo citizens.ResultsFrequent (≥3 times/week) use of staff canteens was most likely among men, younger workers and those in the highest education and income groups. However, after adjustment for demographic, socio-economic and lifestyle factors, those with highest education were least likely to use staff canteens frequently. Frequent eating in staff canteens was positively associated with a Western food pattern (based on fat-rich food, fast food and red meat) and inversely associated with a traditional food pattern (based on boiled potatoes and gravy, and less rice, pasta and oil) in multivariate analyses. Unadjusted, frequent eating in staff canteens was also inversely associated with a prudent food pattern (based on fruit, vegetables, fish, legumes and oil). The likelihood of being obese (BMI ≥ 30 kg/m2) increased significantly with frequent eating in staff canteens, also when adjusted for demographic and socio-economic variables. Adjustment for the food patterns attenuated this relationship, but it was still significant.ConclusionsFrequent eating in staff canteens was negatively related to socio-economic position and positively associated with unhealthy dietary habits. This partly explained higher odds for obesity among frequent users of staff canteens. Future research should assess the availability and food options of staff canteens.

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Khajuria, Rakshit, Anuj Sharma, Sunny Sharma, Ashok Sharma, Jyoti Narayan Baliya, and Parveen Singh. "Performance analysis of frequent pattern mining algorithm on different real-life dataset." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 3 (March 1, 2023): 1355. http://dx.doi.org/10.11591/ijeecs.v29.i3.pp1355-1363.

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<span lang="EN-US">The efficient finding of common patterns: a group of items that appear frequently in a dataset is a critical task in data mining, especially in transaction datasets. The goal of this paper is to look into the efficiency of various algorithms for frequent pattern mining in terms of computing time and memory consumption, as well as the problem of how to apply the algorithms to different datasets. In this paper, the algorithms investigated for mining the frequent patterns are; Pre-post, Pre-post+, FIN, H-mine, R-Elim, and estDec+ algorithms. These algorithms have been implemented and tested on four real-life datasets that are: The retail dataset, the Accidents dataset, the Chess dataset, and the Mushrooms dataset. From the results, it has been observed that, for the Retail dataset, estDec+ algorithm is the fastest among all algorithms in terms of run time as well as consumes less memory for its execution. Pre-post+ algorithm performs better than all other algorithms in terms of run time and maximum memory for the Mushrooms dataset. Pre-Post outperforms other algorithms in terms of performance. And for Accident datasets, in terms of execution time and memory consumption, the FIN method outperforms other algorithms.</span>

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Munson, Benjamin. "Phonological Pattern Frequency and Speech Production in Adults and Children." Journal of Speech, Language, and Hearing Research 44, no. 4 (August 2001): 778–92. http://dx.doi.org/10.1044/1092-4388(2001/061).

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Recent studies have suggested that both adults and children are sensitive to information about phonological pattern frequency; however, the influence of phonological pattern frequency on speech production has not been studied extensively. The current study examined the effect of phonological pattern frequency on the fluency and flexibility of speech production. Normal- and fastrate nonsense-word repetitions of three groups of participants (preschool children, school-aged children, and adults) were analyzed. Subjective ratings of the wordlikeness of nonsense words, percentage phonemes correctly repeated, mean duration, and durational variability were measured. In the first experiment, ratings of the wordlikeness of nonsense words were found to correlate with the pattern frequency of sequences embedded in them. In the second analysis, it was found that children, but not adults, repeated infrequent sequences of phonemes less accurately than frequent sequences. In the third experiment, infrequent sequences were produced with longer durations than frequent ones, with children demonstrating a larger difference between frequent and infrequent sequences than adults. Phonological pattern frequency also influenced variability in duration: infrequent sequences of sounds were more variable than frequent ones. Thus, there appears to be an influence of phonological pattern frequency on speech, and, for some measures, a larger effect size is noted for children.

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Cui, Guan Xun, Qian Wu, Bo He, and Wei Ni. "An Efficient Parallel Algorithm for Mining Frequent Pattern." Advanced Materials Research 562-564 (August 2012): 876–81. http://dx.doi.org/10.4028/www.scientific.net/amr.562-564.876.

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Extraction of frequent patterns in transaction-oriented database is crucial to several data mining tasks such as association rule generation, time series analysis, classification, etc. An Efficient Parallel algorithm for Mining frequent pattern (EPM) was proposed and Fast Distributed association rules Mining (FDM) algorithm was improved. Hash table technology was used to improve the generation efficiency of the 2nd candidate items . It also reduces the number of transactions in transaction database using Tid table technology. A master-slave model of parallel algorithm for mining association rules is designed in the algorithm to reduce the communication cost. The experimental results show that this algorithm has a high efficiency to deal with large database.

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Wisda, Wisda, and Mashud Mashud. "Designing an Application for Analyzing Consumer Spending Patterns Using the Frequent Pattern Growth Algorithm." Jurnal Penelitian Pos dan Informatika 9, no. 2 (December 30, 2019): 151. http://dx.doi.org/10.17933/jppi.2019.090206.

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<p class="JGI-AbstractIsi">In this modern era, the market has been growing rapidly which can be seen from the navel shopping that is lined up in the hearts of big cities such as supermarkets, grocery stores and others that are provided to meet people's needs for primary goods that are always needed at all times. One of them is Giant Express Tamalanrea, a supermarket in the city of Makassar that serves the sale of household goods and general needs. With the use of customer data analysis to determine the customers' purchasing patterns, Giant Express can optimize the collation of goods, by positioning goods at closer shelves based on the level of frequency of goods purchased together by customers. Therefore, this study suggests the creation of an application to analyze consumer spending patterns using the frequent pattern growth algorithm method to ensure appropriate placement of goods to increase sales at Giant Express Tamalanrea. The purpose of this study is to develop an application that can analyze consumer spending patterns to increase sales by positioning goods based on consumer shopping patterns, as well as implementing the Frequent Pattern Growth Algorithm method to determine customer spending patterns to increase sales. Stages of research methods conducted begin with data collection at the study site, system requirements analysis, system design with UML, and system testing with the Black Box method.</p>

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Wisda, Wisda, and Mashud Mashud. "Designing an Application for Analyzing Consumer Spending Patterns Using the Frequent Pattern Growth Algorithm." Jurnal Penelitian Pos dan Informatika 9, no. 2 (December 30, 2019): 151–59. http://dx.doi.org/10.17933/jppi.v9i2.285.

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In this modern era, the market has been growing rapidly which can be seen from the navel shopping that is lined up in the hearts of big cities such as supermarkets, grocery stores and others that are provided to meet people's needs for primary goods that are always needed at all times. One of them is Giant Express Tamalanrea, a supermarket in the city of Makassar that serves the sale of household goods and general needs. With the use of customer data analysis to determine the customers' purchasing patterns, Giant Express can optimize the collation of goods, by positioning goods at closer shelves based on the level of frequency of goods purchased together by customers. Therefore, this study suggests the creation of an application to analyze consumer spending patterns using the frequent pattern growth algorithm method to ensure appropriate placement of goods to increase sales at Giant Express Tamalanrea. The purpose of this study is to develop an application that can analyze consumer spending patterns to increase sales by positioning goods based on consumer shopping patterns, as well as implementing the Frequent Pattern Growth Algorithm method to determine customer spending patterns to increase sales. Stages of research methods conducted begin with data collection at the study site, system requirements analysis, system design with UML, and system testing with the Black Box method.

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Sudhamathy, G., and C. Jothi Venkateswaran. "Hierarchical Frequent Pattern Analysis of Web Logs for Efficient Interestingness Prediction." International Journal of Web Technology 001, no. 001 (June 15, 2012): 19–23. http://dx.doi.org/10.20894/ijwt.104.001.001.006.

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Ardiantoro, L., and N. Sunarmi. "Badminton player scouting analysis using Frequent Pattern growth (FP-growth) algorithm." Journal of Physics: Conference Series 1456 (January 2020): 012023. http://dx.doi.org/10.1088/1742-6596/1456/1/012023.

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Sun, Song, and Joseph Zambreno. "Design and Analysis of a Reconfigurable Platform for Frequent Pattern Mining." IEEE Transactions on Parallel and Distributed Systems 22, no. 9 (September 2011): 1497–505. http://dx.doi.org/10.1109/tpds.2011.34.

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Zhang, Jifu, Xujun Zhao, Sulan Zhang, Shu Yin, and Xiao Qin. "Interrelation analysis of celestial spectra data using constrained frequent pattern trees." Knowledge-Based Systems 41 (March 2013): 77–88. http://dx.doi.org/10.1016/j.knosys.2012.12.013.

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Kankane, Samiksha, and Vikram Garg. "An Enhanced Frequent Pattern Analysis Technique from the Web Log Data." International Journal of Computer Applications 131, no. 15 (December 17, 2015): 7–9. http://dx.doi.org/10.5120/ijca2015906904.

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Sudhamathy, G., and C. Jothi Venkateswaran. "An Efficient Hierarchical Frequent Pattern Analysis Approach for Web Usage Mining." International Journal of Computer Applications 43, no. 15 (April 30, 2012): 1–7. http://dx.doi.org/10.5120/6176-8603.

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KRIBII, Rajae, and Youssef FAKIR. "Mining Frequent Sequential Patterns." Journal of Big Data Research 1, no. 2 (March 15, 2021): 20–37. http://dx.doi.org/10.14302/issn.2768-0207.jbr-21-3455.

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In recent times, the urge to collect data and analyze it has grown. Time stamping a data set is an important part of the analysis and data mining as it can give information that is more useful. Different mining techniques have been designed for mining time-series data, sequential patterns for example seeks relationships between occurrences of sequential events and finds if there exist any specific order of the occurrences. Many Algorithms has been proposed to study this data type based on the apriori approach. In this paper we compare two basic sequential algorithms which are General Sequential algorithm (GSP) and Sequential PAttern Discovery using Equivalence classes (SPADE). These two algorithms are based on the Apriori algorithms. Experimental results have shown that SPADE consumes less time than GSP algorithm.

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Nidhi. R, Ms, and Ms Kanchana V. "Analysis of Road Accidents Using Data Mining Techniques." International Journal of Engineering & Technology 7, no. 3.10 (July 15, 2018): 40. http://dx.doi.org/10.14419/ijet.v7i3.10.15626.

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Road Accident is an all-inclusive disaster with consistently raising pattern. In India according to Indian road safety campaign every minute there is a road accident and almost 17 people die per hour in road accidents. There are different categories of vehicle accidents like rear end, head on and rollover accidents. The state recorded police reports or FIR’s are the documents which contains the information about the accidents. The incident may be self-reported by the people or recorded by the state police. In this paper the frequent patterns of road accidents is been predicted using Apriori and Naïve Bayesian techniques. This pattern will help the government or NGOs to improve the safety and take preventive measures in the roads that have major accident zones.

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Krishna, Bhukya. "Comparative Analysis of Frequent Pattern Matching Based On Apriori & Enhanced Algorithms." International Journal for Research in Applied Science and Engineering Technology V, no. XI (November 22, 2017): 1809–18. http://dx.doi.org/10.22214/ijraset.2017.11262.

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G., Ayman. "A Data Mining Analysis of ERP System using Frequent Pattern Growth Algorithm." International Journal of Computer Applications 182, no. 26 (November 15, 2018): 30–35. http://dx.doi.org/10.5120/ijca2018918154.

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Ahmed, Shafiul Alom, and Bhabesh Nath. "Identification of adverse disease agents and risk analysis using frequent pattern mining." Information Sciences 576 (October 2021): 609–41. http://dx.doi.org/10.1016/j.ins.2021.07.061.

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Pyun, Gwangbum, and Unil Yun. "Analysis and Evaluation of Frequent Pattern Mining Technique based on Landmark Window." Journal of Internet Computing and Services 15, no. 3 (June 30, 2014): 101–7. http://dx.doi.org/10.7472/jksii.2014.15.3.101.

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Suzuki, Nobuo, and Hiromi Matsuno. "Radio Wave Environment Analysis at Different Locations Based on Frequent Pattern Mining." Procedia Computer Science 112 (2017): 1396–403. http://dx.doi.org/10.1016/j.procs.2017.08.061.

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Chen, Hsuan-Sheng, and Wen-Jiin Tsai. "Incorporating frequent pattern analysis into multimodal HMM event classification for baseball videos." Multimedia Tools and Applications 75, no. 9 (January 13, 2015): 4913–32. http://dx.doi.org/10.1007/s11042-015-2447-2.

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Et. al., Divvela Srinivasa Rao,. "A SURVEY ON FREQUENT ITEM SET MINING FOR LARGE TRANSACTIONAL DATA." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (April 10, 2021): 885–93. http://dx.doi.org/10.17762/itii.v9i2.426.

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In the decision making process the Data Analytics plays an important role. The Insights that are obtained from pattern analysis gives many benefits like cost cutting, good revenue, and better competitive advantage. On the other hand the patterns of frequent itemsets that are hidden consume more time for extraction when data increases over time. However less memory consumption is required for mining the patterns of frequent itemsets because of heavy computation. Therefore, an algorithm required must be efficient for mining the patterns of the frequent itemsets that are hidden which takes less memory with short run time. This paper presents a review of different algorithms for finding Frequent Patterns so that a more efficient algorithm for finding frequent items sets can be developed.

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Astuti, Tri, and Lisdya Anggraini. "Analysis of Sequential Book Loan Data Pattern Using Generalized Sequential Pattern (GSP) Algorithm." IJIIS: International Journal of Informatics and Information Systems 2, no. 1 (March 1, 2019): 17–23. http://dx.doi.org/10.47738/ijiis.v2i1.10.

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As a center for learning and information services, STMIK Amikom Purwokerto Library is a source of learning and a source of intellectual activity that is very important for the entire academic community in supporting the achievement of the college Tridharma program. Book lending transaction data, can produce information that is important as supporting decision making when further analyzed. One useful information is that it can provide information in the form of user behavior patterns in borrowing books that are used to maintain the availability of related book stocks to be balanced. This study uses the Generalized Sequential Pattern (GSP) algorithm, which can be used to determine the behavior patterns of users in each transaction and can show relationships or associations between books, both requested simultaneously and sequentially. From the calculations that have been done, 295 frequent sequences are consisting of 3 sequence patterns that are formed from the minimum support of 0.53% or the minimum number of books borrowed, namely 2 books. Three book items have very strong linkages in book lending transactions, namely book code 6690, 2026, and 8131.

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Osler, Merete, Berit L. Heitmann, Lars U. Gerdes, Lillian M. Jørgensen, and Marianne Schroll. "Dietary patterns and mortality in Danish men and women: a prospective observational study." British Journal of Nutrition 85, no. 2 (February 2001): 219–25. http://dx.doi.org/10.1079/bjn2000240.

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The analysis of dietary patterns emerged recently as a possible approach to examining diet–disease relation. We analysed the risk of all-cause and cardiovascular mortality associated with dietary patterns in men and women, while taking a number of potential confounding variables into account. Data were from a prospective cohort study with follow-up of total and cause-specific mortality. A random sample of 3698 men and 3618 women aged 30–70 years and living in Copenhagen County, Denmark, were followed from 1982 to 1998 (median 15 years). Three dietary patterns were identified from a twenty-eight item food frequency questionnaire, collected at baseline: (1) a predefined healthy food index, which reflected daily intakes of fruits, vegetables and wholemeal bread, (2) a prudent and (3) a Western dietary pattern derived by principal component analysis. The prudent pattern was positively associated with frequent intake of wholemeal bread, fruits and vegetables, whereas the Western was characterized by frequent intakes of meat products, potatoes, white bread, butter and lard. Among participants with complete information on all variables, 398 men and 231 women died during follow-up. The healthy food index was associated with reduced all-cause mortality in both men and women, but the relations were attenuated after adjustment for smoking, physical activity, educational level, BMI, and alcohol intake. The prudent pattern was inversely associated with all-cause and cardiovascular mortality after controlling for confounding variables. The Western pattern was not significantly associated with mortality. This study partly supports the assumption that overall dietary patterns can predict mortality, and that the dietary pattern associated with the lowest risk is the one which is in accordance with the current recommendations for a prudent diet.

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Aljuaid, Hanan, and Hanan A. Hosni Mahmoud. "Methodology for Exploring Patterns of Epigenetic Information in Cancer Cells Using Data Mining Technique." Healthcare 9, no. 12 (November 29, 2021): 1652. http://dx.doi.org/10.3390/healthcare9121652.

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Epigenetic changes are a necessary characteristic of all cancer types. Tumor cells usually target genetic changes and epigenetic alterations as well. It is most beneficial to identify epigenetic similar features among cancer various types to be able to discover the appropriate treatments. The existence of epigenetic alteration profiles can aid in targeting this goal. In this paper, we propose a new technique applying data mining and clustering methodologies for cancer epigenetic changes analysis. The proposed technique aims to detect common patterns of epigenetic changes in various cancer types. We demonstrated the validation of the new technique by detecting epigenetic patterns across seven cancer types and by determining epigenetic similarities among various cancer types. The experimental results demonstrate that common epigenetic patterns do exist across these cancer types. Additionally, epigenetic gene analysis performed on the associated genes found a strong relationship with the development of various types of cancer and proved high risk across the studied cancer types. We utilized the frequent pattern data mining approach to represent cancer types compactly in the promoters for some epigenetic marks. Utilizing the built frequent pattern item set, the most frequent items are identified and yield the group of the bi-clusters of these patterns. Experimental results of the proposed method are shown to have a success rate of 88% in detecting cancer types according to specific epigenetic pattern.

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Lu, Yue, Long Zhao, Zhao Li, and Xiangjun Dong. "Genetic Similarity Analysis Based on Positive and Negative Sequence Patterns of DNA." Symmetry 12, no. 12 (December 16, 2020): 2090. http://dx.doi.org/10.3390/sym12122090.

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Similarity analysis of DNA sequences can clarify the homology between sequences and predict the structure of, and relationship between, them. At the same time, the frequent patterns of biological sequences explain not only the genetic characteristics of the organism, but they also serve as relevant markers for certain events of biological sequences. However, most of the aforementioned biological sequence similarity analysis methods are targeted at the entire sequential pattern, which ignores the missing gene fragment that may induce potential disease. The similarity analysis of such sequences containing a missing gene item is a blank. Consequently, some sequences with missing bases are ignored or not effectively analyzed. Thus, this paper presents a new method for DNA sequence similarity analysis. Using this method, we first mined not only positive sequential patterns, but also sequential patterns that were missing some of the base terms (collectively referred to as negative sequential patterns). Subsequently, we used these frequent patterns for similarity analysis on a two-dimensional plane. Several experiments were conducted in order to verify the effectiveness of this algorithm. The experimental results demonstrated that the algorithm can obtain various results through the selection of frequent sequential patterns and that accuracy and time efficiency was improved.

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YADA, KATSUTOSHI, HIROSHI MOTODA, TAKASHI WASHIO, and ASUKA MIYAWAKI. "CONSUMER BEHAVIOR ANALYSIS BY GRAPH MINING TECHNIQUE." New Mathematics and Natural Computation 02, no. 01 (March 2006): 59–68. http://dx.doi.org/10.1142/s1793005706000294.

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In this paper, we discuss how graph mining system is applied to sales transaction data so as to understand consumer behavior. First, existing research of consumer behavior analysis for sequential purchase pattern is reviewed. Then we propose to represent the complicated customer purchase behavior by a directed graph retaining temporal information in a purchase sequence and apply a graph mining technique to analyze the frequent occurring patterns. In this paper, we demonstrate through the case of healthy cooking oil analysis how graph mining technology helps us understand complex purchase behavior.

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Alam, Md Tanvir, Chowdhury Farhan Ahmed, and Md Samiullah. "A Vertex-extension based Algorithm for Frequent Pattern Mining from Graph Databases." Dhaka University Journal of Applied Science and Engineering 7, no. 1 (February 1, 2023): 58–65. http://dx.doi.org/10.3329/dujase.v7i1.62887.

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Frequent pattern mining is a core problem in data mining. Algorithms for frequent pattern mining have been proposed for itemsets, sequences, and graphs. However, existing graph mining frameworks follow an edge-growth approach to building patterns which limits many applications. Motivated by real-life problems, in this work, we define a novel graph mining framework that incorporates vertex-based extensions along with the edge-growth approach. We also propose an efficient algorithm for mining frequent subgraphs. To deal with the exploding search space, we introduce a canonical labeling technique for isomorphic candidates as well as downward closure property-based search space pruning. We present an experimental analysis of our algorithm on real-life benchmark graph datasets to demonstrate the performance in terms of runtime. DUJASE Vol. 7(1) 58-65, 2022 (January)

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Coe, David, Mathew Barlow, Laurie Agel, Frank Colby, Christopher Skinner, and Jian-Hua Qian. "Clustering Analysis of Autumn Weather Regimes in the Northeast United States." Journal of Climate 34, no. 18 (September 2021): 7587–605. http://dx.doi.org/10.1175/jcli-d-20-0243.1.

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AbstractA k-means clustering method is applied to daily ERA5 500-hPa heights, sea level pressure, and 850-hPa winds, 1979–2008, to identify characteristic weather types (WTs) for September–November for the northeast United States. The resulting WTs are analyzed in terms of structure, frequency of occurrence, typical progressions, precipitation and temperature characteristics, and relation to teleconnections. The WTs are used to make a daily circulation-based distinction between early and late autumn and consider shifts in seasonality. Seven WTs are identified for the autumn season, representing a range of trough and ridge patterns. The largest average values of precipitation and greatest likelihood of extremes occur in the Midwestern Trough and Atlantic Ridge patterns. The greatest likelihood of extreme temperatures occurs in the Northeast Ridge. Some WTs are strongly associated with the phase of the North Atlantic Oscillation and Pacific–North America pattern, with frequency of occurrence for several WTs changing by more than a factor of 2. The two most common progressions between the WTs are one most frequent in September, Mid-Atlantic Trough to Northeast Ridge to Mid-Atlantic Trough, and one most frequent in mid-October–November, Midwestern Trough to Northeast Trough to Midwestern Trough. This seasonality allows for a daily WT-based distinction between early and late season. A preliminary trend analysis indicates an increase in early season WTs later in the season and a decrease in late season WTs earlier in the season; that is, a shift toward a longer period of warm season patterns and a shorter, delayed period of cold season patterns.

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Journal articles on the topic 'Frequent pattern analysis'

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