{"title":"Itemset Mining","authors":"","doi":"10.1017/9781108564175.011","DOIUrl":"https://doi.org/10.1017/9781108564175.011","url":null,"abstract":"In many applications one is interested in how often two or more objects of interest co-occur. For example, consider a popular web site, which logs all incoming traffic to its site in the form of weblogs. Weblogs typically record the source and destination pages requested by some user, as well as the time, return code whether the request was successful or not, and so on. Given such weblogs, one might be interested in finding if there are sets of web pages that many users tend to browse whenever they visit the web site. Such \" frequent \" sets of web pages give clues to the user browsing behavior and can be used for improving the user's browsing experience. The quest to mine frequent patterns appears in many other domains. The proto-typical application is market basket analysis, i.e., to mine the sets of items that are frequently bought together, at a supermarket by analyzing the customer shopping carts (the so-called \" market baskets \"). Once we mine the frequent sets, they allow us to extract association rules among the item sets, where we make some statement about how likely are two sets of items to co-occur or to conditionally occur. For example, in the weblog scenario frequent sets allow us to extract rules like, \" Users who visit the sets of pages main, laptops and rebates also visit the pages shopping-cart and checkout \" , indicating, perhaps, that the special rebate offer is resulting in more laptop sales. In the case of market baskets, we can find rules like, \" Customers who buy Milk and Cereal also tend to buy Bananas \" , which may prompt a grocery store to co-locate bananas in the cereal aisle. Let I = {x 1 , x 2 , · · · , x m } be a set of binary-valued attributes, which we call items. A set X ⊆ I is called an itemset. The set of items I may denote, for example, the collection of all products sold at a supermarket, the set of all web pages at a DRAFT @ 2012-10-14 21:52. Please do not distribute. Feedback is Welcome. Note that this book shall be available for purchase from Cambridge University Press and other standard distribution channels, that no unauthorized distribution shall be allowed, and that the reader may make one copy only for personal on-screen use.","PeriodicalId":381018,"journal":{"name":"Data Mining and Machine Learning","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131732843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Clustering","authors":"Jan Žižka, F. Dařena, Arnošt Svoboda","doi":"10.1002/9781119296294.ch5","DOIUrl":"https://doi.org/10.1002/9781119296294.ch5","url":null,"abstract":"We present the first practical algorithm for the optimal linear leaf ordering of trees that are generated by hierarchical clustering. Hierarchical clustering has been extensively used to analyze gene expression data, and we show how optimal leaf ordering can reveal biological structure that is not observed with an existing heuristic ordering method. For a tree with n leaves, there are 2 n − 1 linear orderings consistent with the structure of the tree. Our optimal leaf ordering algorithm runs in time O ( n 4 ) , and we present further improvements that make the running time of our algo-rithm practical.","PeriodicalId":381018,"journal":{"name":"Data Mining and Machine Learning","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124572648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Representative-based Clustering","authors":"Mohammed J. Zaki, Wagner Meira Jr","doi":"10.1017/cbo9780511810114.014","DOIUrl":"https://doi.org/10.1017/cbo9780511810114.014","url":null,"abstract":"Given a dataset with n points in a d-dimensional space D = {x i } n i=1 , and given the number of desired clusters k, the goal of representative-based clustering is to partition the points into k groups. Each such partitioning of the points into k groups or clusters is called a clustering, denoted as C = {C 1 , C 2 , · · · , C k }. Further, for each cluster C i there exists a representative point that summarizes the cluster, a common choice being the mean (also called the centroid) µ i of all points in the cluster.","PeriodicalId":381018,"journal":{"name":"Data Mining and Machine Learning","volume":"226 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116217673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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