Relevant bibliographies by topics / Cross modal association mining

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Cross modal association mining'

Author: Grafiati
Published: 4 June 2021
Last updated: 4 February 2022

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Journal articles on the topic "Cross modal association mining"

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Yao, Ran Bo, An Ping Song, Xue Hai Ding, and Ming Bo Li. "Cross Sellingusing Association Rule Mining." Applied Mechanics and Materials 687-691 (November 2014): 1337–41. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1337.

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In the retail enterprises, it is an important problem to choose goods group through their sales record.We should consider not only the direct benefits of product, but also the benefits bring by the cross selling. On the base of the mutual promotion in cross selling, in this paper we propose a new method to generate the optimal selected model. Firstly we use Apriori algorithm to obtain the frequent item sets and analyses the association rules sets between products.And then we analyses the above results to generate the optimal products mixes and recommend relationship in cross selling. The experimental result shows the proposed method has some practical value to the decisions of cross selling.
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Cheng, Yang, Ming Cheng, Tao Pang, and Sizhen Liu. "Using Clustering Analysis and Association Rule Technology in Cross-Marketing." Complexity 2021 (April 21, 2021): 1–11. http://dx.doi.org/10.1155/2021/9979874.

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In this paper, according to the perspective of customers and products, by using clustering analysis and association rule technology, this paper proposes a cross-marketing model based on an improved sequential pattern mining algorithm, where an improved algorithm AP (Apriori all PrefixSpan) is applied. The algorithm can reduce the time cost of constructing a projection database and the influence of the increase of support on the algorithm efficiency. The improved idea is that when the first partition is used to generate the projection database, the number of itemsets in the projection database is sorted from small to large, and when the second partition is used, the sequence patterns are generated directly from the mined sequence patterns, so as to reduce the construction of the database. The experimental results show that this method can quickly mine the effective information in complex data sets, improve the accuracy and efficiency of data mining, and occupy less memory consumption, which has good theoretical value and application value.
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Amseke, Robynson, and Edi Winarko. "Aplikasi Algoritma CBA untuk Klasifikasi Resiko Pemberian Kredit (Studi kasus: PT. Telkom CDC Sub Area Kupang)." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 8, no. 2 (2014): 121. http://dx.doi.org/10.22146/ijccs.6540.

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AbstrakSalah satu penyebab kredit bermasalahberasal dari pihak internal, yaitu kurang telitinya timdalam melakukan survei dan analisis, atau bisa juga karena penilaian dan analisis yang bersifat subjektif.Penyebab ini dapat diatasi dengan sistem komputer, yaitu aplikasi komputer yang menggunakan teknik data mining.Teknik data mining digunakan dalam penelitian ini untuk klasifikasi resiko pemberian kredit dengan menerapkan algoritma Classification Based On Association (CBA). Algoritma ini merupakan salah satu algoritma klasifikasi dalam data mining yang mengintegrasikan teknik asosiasi dan klasifikasi. Data kredit awal yang telah di-preprocessing, diproses menggunakan algoritma CBA untuk membangun model, lalu model tersebut digunakan untuk mengklasifikasi data pelaku usaha baru yang mengajukan kredit ke dalam kelas lancar atau macet.Teknik Pengujian akurasi model diukur menggunakan 10-fold cross validation. Hasil pengujian menunjukkan bahwa rata-rata nilai akurasi menggunakan algoritma CBA (57,86%), sedikit lebih tinggi dibandingkan rata-rata nilai akurasi menggunakan algoritma Naive Bayes dan SVM dari perangkat lunak Rapid Miner 5.3 (56,35% dan 55,03%). Kata kunci—classification based on association, CBA, data mining, klasifikasi, resiko pemberian kredit AbstractOne of the causes of non-performing loans come from the internal, that is caused by a lack of rigorous team in conducting the survey and analysis, or it could be due to subjective evaluation and analysis. The cause of this can be solved by a computer system, the computer application that uses data mining techniques. Data mining technique, was usedin this study toclassifycreditriskby applyingalgorithmsClassificationBasedonAssociation(CBA). This algorithm is an algorithm classification of data mining which integratingassociationandclassificationtechniques. Preprocessed initial-credit data, will be processed using theCBAalgorithmto create a model of which is toclassifythe newloandata into swift class or bad one. Testing techniques the accuracy of the model was measured by 10-fold cross validation. The resultshowsthatthe accuracy averagevalue using theCBAalgorithm(57,86%), was slightly higher than those using thealgorithmsofSVM andNaiveBayes from RapidMiner5.3software(56,35% and55,03%, respectively). Keywords—classification based on association, CBA, data mining, classification, credit risk
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Islam, Arif Ul, Shamim H. Ripon, and Nuruddin Qaisar Bhuiyan. "Cervical Cancer Risk Factors: Classification and Mining Associations." APTIKOM Journal on Computer Science and Information Technologies 4, no. 1 (2020): 8–18. http://dx.doi.org/10.34306/csit.v4i1.85.

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Women of all over the world suffer from a common cancer, named Cervical cancer. Cervical cancer cellsgrow slowly at the cervix. This cancer can be avoided if it is recognized and handled in its first stage. Now it is a keychallenge for Medical experts to identify such cancer before it develops extremely. Nowadays, data mining modelsare popularly used to extract hidden patterns from huge medical dataset. This paper introduces data miningtechinques for classification and finding associations in order to detect Cervical cancer at early stage. Afterpreprocessing, the dataset was tested on Decision Tree, Random Forest, Logistic Model Tree and Artificial NeuralNetwork. These methods achieve considerable success in case of both K-fold cross validations and randomly splitdataset. Association rules has been established for detecting comparatively riskier factors which are moreresponsible for cancer development. The proposed methodology can help Medical experts to conduct their researchon Cervical cancer.
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Guo, Chun Fen, and Li Chen Zhuang. "Research on Mining Method of Process Knowledge Based on Ontology." Applied Mechanics and Materials 401-403 (September 2013): 1470–73. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1470.

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Making use of hierarchical structure clearly of concept model of ontology , on the basis of Apriori algorithm and introducing cross linker method, an mining algorithm of process knowledge of association rule is proposed based on ontology. To verify the feasibility of the algorithm, partial correlation process knowledge is dicovered by this algorithm.
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-Ul-Islam, Arif, Shamim H Ripon, and Nuruddin Qaisar Bhuiyan. "Cervical Cancer Risk Factors: Classification and Mining Associations." APTIKOM Journal on Computer Science and Information Technologies 4, no. 1 (2019): 8–18. http://dx.doi.org/10.11591/aptikom.j.csit.131.

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Women of all over the world suffer from a common cancer, named Cervical cancer. Cervical cancer cells grow slowly at the cervix. This cancer can be avoided if it is recognized and handled in its first stage. Now it is a key challenge for Medical experts to identify such cancer before it develops extremely. Nowadays, data mining models are popularly used to extract hidden patterns from huge medical dataset. This paper introduces data mining techinques for classification and finding associations in order to detect Cervical cancer at early stage. After preprocessing, the dataset was tested on Decision Tree, Random Forest, Logistic Model Tree and Artificial Neural Network. These methods achieve considerable success in case of both K-fold cross validations and randomly split dataset. Association rules has been established for detecting comparatively riskier factors which are more responsible for cancer development. The proposed methodology can help Medical experts to conduct their research on Cervical cancer.
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Chen, Wei, Zhengtao Yu, Yantuan Xian, Zhenhan Wang, and Yonghua Wen. "Mining Keywords from Short Text Based on LDA-Based Hierarchical Semantic Graph Model." International Journal of Information Systems in the Service Sector 12, no. 2 (2020): 76–87. http://dx.doi.org/10.4018/ijisss.2020040106.

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Extracting keywords from a text set is an important task. Most of the previous studies extract keywords from a single text. Using the key topics in the text collection, the association relationship between the topic and the topic in the cross-text, and the association relationship between the words and the words in the cross-text has not played an important role in the previous method of extracting keywords from the text collection. In order to improve the accuracy of extracting keywords from text collections, using the semantic relationship between topics and topics in texts and highlighting the semantic relationship between words and words under the key topics, this article proposes an unsupervised method for mining keywords from short text collections. In this method, a two level semantic association model is used to link the semantic relations between topics and the semantic relations between words, and extract the key words based on the combined action. First, the text is represented with LDA; the authors used word2vec to calculate the semantic association between topic and topic, and build a semantic relation graph between topics, that is the upper level graph, and use a graph ranking algorithm to calculate each topic score. In the lower layer, the semantic association between words and words is calculated by using the topic scores and the relationship between topics in the upper network allow a graph to be constructed. Using a graph sorting algorithm sorts the words in short text sets to determine the keywords. The experimental results show that the method is better for extracting keywords from the text set, especially in short articles. In the text, the important topics, the relationship between topics and the correlation between words can improve the accuracy of extracting keywords from the text set.
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Li, Chenghao, Yiding Wang, Changwei Miao, and Cheng Huang. "Cross-Site Scripting Guardian: A Static XSS Detector Based on Data Stream Input-Output Association Mining." Applied Sciences 10, no. 14 (2020): 4740. http://dx.doi.org/10.3390/app10144740.

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The largest number of cybersecurity attacks is on web applications, in which Cross-Site Scripting (XSS) is the most popular way. The code audit is the main method to avoid the damage of XSS at the source code level. However, there are numerous limits implementing manual audits and rule-based audit tools. In the age of big data, it is a new research field to assist the manual auditing through machine learning. In this paper, we propose a new way to audit the XSS vulnerability in PHP source code snippets based on a PHP code parsing tool and the machine learning algorithm. We analyzed the operation sequence of source code and built a model to acquire the information that is most closely related to the XSS attack in the data stream. The method proposed can significantly improve the recall rate of vulnerability samples. Compared with related audit methods, our method has high reusability and excellent performance. Our classification model achieved an F1 score of 0.92, a recall rate of 0.98 (vulnerable sample), and an area under curve (AUC) of 0.97 on the test dataset.
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Wang, Hao, Xin Ning Su, and San Hong Deng. "Study on Hotspots Detection Based on CSSCI Academic Resource Ontology." Advanced Materials Research 171-172 (December 2010): 19–26. http://dx.doi.org/10.4028/www.scientific.net/amr.171-172.19.

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Cross relation and its blending degree among academic resource would be revealed through association analysis of academic resource from CSSCI. So, this paper tries to change the traditional analysis mode that using association rule mining to gain the subject relationship based on single standard, and introduces the ontology mechanism with semantic description capabilities into the knowledge organization of CSSCI academic resource for organizing all kinds of academic concepts by object-oriented approach, so that to establishes CSSCI Academic Resource Networks Model based on Ontology. On this basis, knowledge mining technique will also be adopted to detect the research hotspots of disciplines from the perspectives of high-frequency themes and themes from influential entity in disciplines, to discover the interdisciplinary hotspots so as to promote the exchange of interdisciplinary and to clear the specific direction of interdisciplinary cooperation. Upon that, all kinds of analysis conclusions and scientific laws which have academic value and could generate academic influence would be obtained to support the decision for scientific evaluation.
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Yu, Chong Ho, Hyun Seo Lee, Emily Lara, and Siyan Gan. "Adult Learning Across USA, Canada, and New Zealand: A Cross-Cultural Study of PIAAC." International Education Studies 12, no. 5 (2019): 1. http://dx.doi.org/10.5539/ies.v12n5p1.

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Skeptics of Programme for International Student Assessment (PISA) and Trend for International Math and Science Study (TIMSS) argue that while US elementary and high school students are behind their peers in other nations, the US workforce is still excellent because of the high quality post-secondary educational institutions in the US. However, the Programme for the International Assessment of Adult Competencies (PIAAC) indicates that US adults are in fact far behind their international peers in literacy, numeracy, and technology-based problem solving. Through the use of data mining, this study explored the possible association between PIAAC scores and several constructs. Since the US, Canada, and New Zealand were considered culturally similar nations, according to cluster analysis, patterns between PIAAC scores and selected constructs were analyzed by a variety of big data analytical methods, including cluster analysis, bootstrap forest, boosted tree, and data visualization. Given that PIAAC used multiple computerized adaptive testing, the consequential plausible values were randomly selected when the ensemble approach was used. Additionally, model comparison was utilized to decide between bagging and boosting in order to select the optimal model for each sample. In these samples, cultural engagement, readiness to learn, and social trust, respectively emerged as strong predictors for learning outcomes as they were assessed by PIAAC.
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Dissertations / Theses on the topic "Cross modal association mining"

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Reutterer, Thomas, Kurt Hornik, Nicolas March, and Kathrin Gruber. "A data mining framework for targeted category promotions." Springer, 2016. http://dx.doi.org/10.1007/s11573-016-0823-7.

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This research presents a new approach to derive recommendations for segment-specific, targeted marketing campaigns on the product category level. The proposed methodological framework serves as a decision support tool for customer relationship managers or direct marketers to select attractive product categories for their target marketing efforts, such as segment-specific rewards in loyalty programs, cross-merchandising activities, targeted direct mailings, customized supplements in catalogues, or customized promotions. The proposed methodology requires cus- tomers' multi-category purchase histories as input data and proceeds in a stepwise manner. It combines various data compression techniques and integrates an opti- mization approach which suggests candidate product categories for segment-specific targeted marketing such that cross-category spillover effects for non-promoted categories are maximized. To demonstrate the empirical performance of our pro- posed procedure, we examine the transactions from a real-world loyalty program of a major grocery retailer. A simple scenario-based analysis using promotion responsiveness reported in previous empirical studies and prior experience by domain experts suggests that targeted promotions might boost profitability between 15 % and 128 % relative to an undifferentiated standard campaign.
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Garnier, Marie-Margeride. "La synesthésie chez l'enfant : prévalence, aspects développementaux et cognitifs." Thesis, Toulouse 2, 2016. http://www.theses.fr/2016TOU20004/document.

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Les synesthètes ont la particularité d’associer une expérience supplémentaire (e.g. une couleur) lors de la présence de certains stimuli (e.g. une lettre). L’objectif de cette thèse était de mieux comprendre le développement de la synesthésie à travers trois types : Graphème-Couleur, Graphème-Personnalité et représentation des nombres dans l'espace (« Forme des nombres »). Si la synesthésie Graphème-Couleur a déjà été étudiée chez l’enfant, les deux autres n’ont jamais fait l’objet d’étude développementale auparavant.Les productions graphiques de 797 enfants de dernière année de maternelle et des deux premières années de primaire (CP et CE1) ont été recueillies (2 sessions, 2 à 3 semaines d’écart). Pour tester l’hypothèse d’un continuum entre les synesthètes et la population générale, nous avons aussi étudié les associations d’enfants non synesthètes.Nous avons mis en avant les difficultés méthodologiques de l’étude de la synesthésie chez les enfants entre 5 et 8 ans. Nos résultats ne nous permettent pas d’attester de façon certaine l’existence de la synesthésie dans cette tranche d’âge. Mais nous avons observé une augmentation du nombre de synesthètes potentiels par niveau scolaire, et un niveau de constance bas de leurs associations par rapport à ce qui est observé chez l’adulte. Comme les enfants connaissent bien les lettres et les nombres, du moins en CE1, on peut penser que ces synesthésies ne sont pas seulement liées à l’apprentissage de l’inducteur en tant que tel. Mais elles pourraient se développe en parallèle d’apprentissages complexes (e.g. calcul ou la rédaction), ou en suivant le développement de la mémoire et de l’imagerie mentale<br>Synaesthetes have the peculiarity of associating a supplementary experience (e.g. the colour green) when certain stimuli are presented (e.g. the letter F). The aim of this thesis was to better understand the development of synaesthesia through three types: Grapheme–Colour, Grapheme–Personality and the representation of numbers in space.We collected the graphical representations of 797 children from Kindergarten, 1st Grade, and 2nd Grade twice, at 2-3 weeks interval. To test the hypothesis of a continuum between synaesthetes and the general population, we also studied the associations of non-synaesthete children. We reported the methodological problems specific to the study of synaesthesia in 5 to 8 years old children. We could not definitely attest the existence of synaesthesia in this age group. But we observed a growing number of potential synaesthetes in each grade, as well as a low consistency level of their associations in comparison with adults. Since children know well letters and numbers, at least in the 2nd grade, we can consider that these types of synaesthesia are not only linked to the acquisition of their inducers themselves. But they could develop during harder acquisitions (numeracy or literacy) or with the development of mental imagery and associative memory
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Lin, Sheng-Chieh, and 林聖傑. "Mining Inter-transactional Association Rules among Cross-concept Levels." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/25493504195999825763.

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碩士<br>國立中央大學<br>工業管理研究所<br>93<br>論文名稱：Mining Inter-transactional Association Rules among Cross-concept Levels 頁數：53 校所組別：國立中央大學工業管理研究所 畢業時間及提要別：九十三學年度第二學期碩士論文提要 研究生： 林聖傑 指導教授：沈 國 基 老師 論文提要內容： In our research, we mainly consider the Appearance period of each item in the database and minimum item support (MIS) to mine inter-transactional association rules. In the previous researches of inter-transactional association rules, the methodology they proposed are based on Apriori Algorithm and didn’t with the consideration of product concept hierarchy. The efficiency of their methods is reduced by generating too many candidates for frequent items and the rules wouldn’t give us more detailed information. In our research, we proposed an methodology named ITCL_FP-tree(MIS) for mining inter-transactional association rules not only about items in the atomic level but also cross the concept levels. Considering of each item’s natures of appearance, we count the support of each item with the consideration of the Appearance period of items instead of count the support by the length of the database. This will solve the rare item problem. In our experiment, we use the real-life data for verifying ITCL_FP-tree(MIS) can mine more frequent rules than CL_FP-tree(MIS) and IT_FP-tree(MIS) which we proposed for mining inter-transactional association rules among items in the atomic level of concept hierarchy. And we also use the concept of gap for pruning the frequent but redundant parent items.
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Lin, Hung-Wei, and 林宏韋. "Mining Cross-level Association Rules between Concept Hierarchy Using CL_FP-Tree." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/77309227779556110849.

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碩士<br>國立中央大學<br>工業管理研究所<br>91<br>We study a cross-level association rule mining, given a concept hierarchy of all products sold in a retailer. Some association rules of interest may occur between two different levels, which are usually difficult to find in terms of number of database scans or computing effort. Previous techniques for mining cross-level association rules are mostly top-down, progressive depending method extended from Apriori algorithm. This approach results in worse mining efficiency and incompleteness of mined rules. In this research, we propose a bottom-up, simultaneously merging method based on FP-tree, called CL_FP-tree, to improve the mining efficiency and completeness of mining cross-level association rule. According to the concept hierarchy attributes and different ways for storing the transaction data, we propose different ways to count the support of items at each concept hierarchy level. CL_FP-tree aims to reduce the number of database rescans which are seemingly inevitable as we need to have the cross-level information. After constructing the CL_FP-tree, the application of the known FP-growth algorithm for mining cross-level association rule is then straightforward. We implement the FP-tress based algorithm, CL_FP-tree algorithm, with real data and compare it with Apriori based algorithm, CL_Apriori algorithm. We observe that CL_FP-tree algorithm can mine out more interesting and potential cross-level association rules and more efficient than CL_Apriori algorithm. Besides, we analyze various exceptional conditions as we apply real data to mine cross-level association rules and find out some important and interesting factor.
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Kao, Shih-Hui, and 高詩惠. "Mining non-redundant inter-transaction cross-level association rules with appearance period." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/49407810154905911874.

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碩士<br>國立中央大學<br>工業管理研究所<br>94<br>Most of previous studies on mining association rules are mining intra-transaction associations at the atomic level of concept hierarchy. In this study, we will mine the non-redundant inter-transaction cross-level association rules. An inter-transaction cross-level association rule describes the association relationships among different transactions and the rules among concepts at any level of a hierarchy. Additional step in pruning redundant rule is usually carried out after rules are found. However, this kind of mining may cause generating a large number of potential redundant rules. In retailing, an item may not be carried in the entire year in the shop. Therefore, mining the rules under such situations requires solving the rare item problem. Since all items in the database may not have the same natures or similar frequencies. In real-life applications, some items may appear very frequently and others may appear rarely. To find frequent items which appear rarely, we first identify the appearance period of each item, and then calculate the item’s support value. Multiple minimum support (MIS) is used to reflect the distinct nature of each item. In order to mine interesting rules and to improve the mining efficiency, we adopt the concept of gap to prune redundant and uninteresting items before rule generation rather than remove uninteresting rules after rule mining. Finally, we implement an FP-tree based algorithm, ITCL_FP-tree, on real data. Our experiment shows that we can prune out almost 50 to 70 percent of the redundant and uninteresting rules. The runtime of determining frequent items or generating rule is shorter than the one by using the traditional mining procedures even when the number of transactions is large. The result indicates that we can discover inter-transaction association rules with non-redundant knowledge.
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Li, Sin-Da, and 李信達. "A Bottom Up Algorithm for Mining Cross-Level Association Patterns without Redundant Rules Generation." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/65s577.

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碩士<br>國立東華大學<br>資訊工程學系<br>95<br>Mining multilevel association rules is an interesting domain in data mining. However, the great parts of previous studies are devoted to mine at same level. By the way, many efficient associations will omit or the mined rules not perceive through the senses directly. Only a few works focus on mining cross level associations, and existing works still generate amount of redundant rules. In this thesis, we propose a bottom-up algorithm for mining cross-level association rules at multiple concept levels in large transaction database. Our algorithm, named ML-BottomUp, generates useful rules with high quality and none redundancy. Moreover, the similar rules are also been combined for generating efficient ones. A set of experiments is also performing to show the benefit of our approach.
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Hsueh, Wei-Chun, and 薛頠浚. "Mining Cross-Level Association Rules with Multiple Minimum Supports within the Sold Periods of Products." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/54259651337661015509.

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碩士<br>國立中央大學<br>工業管理研究所<br>92<br>Cross-level association rules mining with multiple minimum supports is an important generalization of the association rule mining problem. Instead of setting a single minimum support for all items, Liu et al. proposed a method, named MSApriori, to allow users for specifying multiple minimum supports to reflect the natures of the items. Because not all items are sold in a whole year, we should consider the transactions in the sold periods of items as we calculate the supports of items. Previous techniques for mining cross-level association rules with multiple minimum supports are most top-down, progressive depending method extended from Apriori algorithm, e.g. MMS_Cumulate. Previous approaches result in worse mining efficiency and incompleteness of mined rules. In this research, we propose a bottom-up, simultaneously merging method based on CL_FP-tree, called CL_FP-tree (MIS), to improve the mining efficiency and completeness of mining cross-level association rule with multiple minimum supports. We extend the procedure, which proposed by Alex H.W. Lin (2003) for supports counting, not only to count the supports for all items, but also to judge the sold periods of all items as the basis of support counting. CL_FP-tree (MIS) aims to reduce the number of database rescans for finding the cross-level information. We implement the CL_FP_tree (MIS) with real data and find the results that the efficiency of CL_FP_tree (MIS) is better than CL_Apriori(MIS). And the number of cross-level association rules found by CL_FP-tree (MIS) algorithm is more than CL_Apripri(MIS). Furthermore, we solve the problem that the number of 1-frequent items decreasing as long as the number of transactions increasing.
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Books on the topic "Cross modal association mining"

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C, Peters, ed. Evaluation of multilingual and multi-modal information retrieval: 7th Workshop of the Cross-Language Evaluation Forum, CLEF 2006, Alicante, Spain, September 20-22, 2006 ; revised selected papers. Springer, 2007.

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Bybee, Joan L. Usage-based Theory and Exemplar Representations of Constructions. Edited by Thomas Hoffmann and Graeme Trousdale. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780195396683.013.0004.

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This chapter outlines a view of Construction Grammar in which the mental grammar of speakers is shaped by the repeated exposure to specific utterances, and in which domain-general cognitive processes such as categorization and cross-modal association play a crucial role in the entrenchment of constructions. Under this view, all linguistic knowledge is viewed as emergent and constantly changing. The chapter emphasizes that the process of chunking along with categorization leads to the creation of constructions. It also provides semantic/pragmatic and phonetic arguments for exemplar representation and a discussion of the role of type and token frequency in determining the structure of the schematic slots in constructions, as well as the productivity of constructions.
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Book chapters on the topic "Cross modal association mining"

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Zhang, Peng-Fei, Zi Huang, and Zheng Zhang. "Semantics-Reconstructing Hashing for Cross-Modal Retrieval." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47436-2_24.

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Ritschard, Gilbert, and Nicolas Nicoloyannis. "Aggregation and Association in Cross Tables." In Principles of Data Mining and Knowledge Discovery. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45372-5_71.

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Cong, Yonghui, Zengchang Qin, Jing Yu, and Tao Wan. "Cross-Modal Information Retrieval – A Case Study on Chinese Wikipedia." In Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35527-1_2.

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Zhang, Xingmeng, Cheng Jin, Yuejie Zhang, and Tao Zhang. "Image Tag Recommendation via Deep Cross-Modal Correlation Mining." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47674-2_36.

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Kaya, Mehmet, and Reda Alhajj. "Effective Mining of Fuzzy Multi-Cross-Level Weighted Association Rules." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11875604_46.

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Lu, Lin, and Lei Cao. "Analysis of the Application and the Association Rule Mining in the Library and Data Management of College of Economics." In Application of Intelligent Systems in Multi-modal Information Analytics. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15740-1_32.

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Agarwal, Reshu. "An Approach for Estimating the Opportunity Cost Using Temporal Association Rule Mining and Clustering." In Encyclopedia of Organizational Knowledge, Administration, and Technology. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3473-1.ch046.

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Timely identification of newly emerging trends is needed in business process. Data mining techniques are best suited for the classification, useful patterns extraction and predications which are very important for business support and decision making. Some research studies have also extended the usage of this concept in inventory management to determine opportunity cost based on association rules. Yet, not many research studies have considered the application of the data mining approach on evaluating penalty cost which is also a significant factor to the manager for optimal inventory control. In this paper, two different cases for evaluating penalty cost based on cross-selling effect are presented. An example is illustrated to validate the results.
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Zuccaro, Cataldo, Michel Plaisent, and Prosper Bernard. "A Preliminary Framework to Fight Tax Evasion in the Home Renovation Market." In Advances in Data Mining and Database Management. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4963-6.ch015.

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This chapter presents a preliminary framework to tackle tax evasion in the field of residential renovation. This industry plays a major role in economic development and employment growth. Tax evasion and fraud are extremely difficult to combat in the industry since it is characterized by a large number of stakeholders (manufacturers, retailers, tradesmen, and households) generating complex transactional dynamics that often defy attempts to deploy transactional analytics to detect anomalies, fraud, and tax evasion. This chapter proposes a framework to apply transactional analytics and data mining to develop standard measures and predictive models to detect fraud and tax evasion. Combining big data sets, cross-referencing, and predictive modeling (i.e., anomaly detection, artificial neural network support vector machines, Bayesian network, and association rules) can assist government agencies to combat highly stealth tax evasion and fraud in the residential renovation.
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Conference papers on the topic "Cross modal association mining"

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He, Ruhan, Naixue Xiong, Tai-hoon Kim, and Yong Zhu. "Mining Cross-Modal Association Rules for Web Image Retrieval." In 2008 International Symposium on Computer Science and its Applications (CSA). IEEE, 2008. http://dx.doi.org/10.1109/csa.2008.70.

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Yang, Pengcheng, Zhihan Zhang, Fuli Luo, Lei Li, Chengyang Huang, and Xu Sun. "Cross-Modal Commentator: Automatic Machine Commenting Based on Cross-Modal Information." In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/p19-1257.

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Liu, Xuanwu, Zhao Li, Jun Wang, Guoxian Yu, Carlotta Domenicon, and Xiangliang Zhang. "Cross-Modal Zero-Shot Hashing." In 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 2019. http://dx.doi.org/10.1109/icdm.2019.00055.

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Li, Dongge, Nevenka Dimitrova, Mingkun Li, and Ishwar K. Sethi. "Multimedia content processing through cross-modal association." In the eleventh ACM international conference. ACM Press, 2003. http://dx.doi.org/10.1145/957013.957143.

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Zeng, Donghuo, and Keizo Oyama. "Learning Joint Embedding for Cross-Modal Retrieval." In 2019 International Conference on Data Mining Workshops (ICDMW). IEEE, 2019. http://dx.doi.org/10.1109/icdmw.2019.00156.

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Fei, Hongliang, Tan Yu, and Ping Li. "Cross-lingual Cross-modal Pretraining for Multimodal Retrieval." In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.naacl-main.285.

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Menon, Aditya Krishna, Didi Surian, and Sanjay Chawla. "Cross-Modal Retrieval: A Pairwise Classification Approach." In Proceedings of the 2015 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2015. http://dx.doi.org/10.1137/1.9781611974010.23.

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Alikhani, Malihe, Piyush Sharma, Shengjie Li, Radu Soricut, and Matthew Stone. "Cross-modal Coherence Modeling for Caption Generation." In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.acl-main.583.

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Yan, Ming, Jitao Sang, and Changsheng Xu. "Mining Cross-network Association for YouTube Video Promotion." In MM '14: 2014 ACM Multimedia Conference. ACM, 2014. http://dx.doi.org/10.1145/2647868.2654920.

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Wang, Yongjin, Ling Guan, and A. N. Venetsanopoulos. "Audiovisual emotion recognition via cross-modal association in kernel space." In 2011 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2011. http://dx.doi.org/10.1109/icme.2011.6011949.

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