01895nas a2200205   4500008004100000245009100041210007000132260000900202300001200211490000700223520122800230653003101458653001001489653001601499653001901515100001401534700001301548700001101561856011701572       2014                        eng d00aResolving rule conflicts based on Naïve Bayesian model for associative classification0 aResolving rule conflicts based on Naïve Bayesian model for assoc  c2014  a36 - 430 v123 aThe rule conflict is an important issue for associative classification due to a large set of rules. In this paper, a new approach called Associative Classification with Bayes (AC-Bayes) is proposed. To address rule conflicts, AC-Bayes has two distinguished features: (1) Associative classification is improved. (2) Naïve Bayesian model is applied in process of classification. A small set of high quality rules is generated by discovering not only frequent but mutual associated itemsets. Therefore, it will reduce the opportunities of rule conflicts. AC-Bayes also selects the best n rules to predict the class value of new instances. When rule conflicts are occurred, we collect those instances covered by these matched rules to form a new training set. Then, it scans new training set to compute the posterior probabilities of each class conditioned on the test instance. The class value that maximizes the posterior probability is assigned to the test instance. The experiments' results show that the improved associative classification decreases significantly the number of rules and AC-Bayes has better average classification accuracy in comparison with associative classification and Naïve Bayesian classification.10aAssociative classification10aBayes10aData mining10aRule conflicts1 aHuang, Z.1 aZhou, Z.1 aHe, T.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84900460217&partnerID=40&md5=bec565675923a67f04838dadf519210d01727nas a2200265   4500008004100000245003900041210003700080260000900117300001200126490000700138520098900145653001501134653001601149653001101165653001801176653001601194653001901210653003801229653001401267653001301281100001701294700001701311700001601328856011701344       2014                        eng d00aSoft-404 pages, a crawling problem0 aSoft404 pages a crawling problem  c2014  a73 - 920 v123 aDuring its traversal of the Web, crawler systems have to deal with multiple challenges. Some of them are related with detecting garbage content to avoid wasting resources processing it. Soft-404 pages are a type of garbage content generated when some web servers do not use the appropriate HTTP response code for death links making them to be incorrectly identified. Our analysis of the Web has revealed that 7.35% of web servers send a 200 HTTP code when a request for an unknown document is received, instead of a 404 code, which indicates that the document is not found. This paper presents a system called Soft404Detector, based on web content analysis to identify web pages that are Soft-404 pages. Our system uses a set of content-based heuristics and combines them with a C4.5 classifier. For testing purposes, we built a Soft-404 pages dataset. Our experiments indicate that our system is very effective, achieving a precision of 0.992 and a recall of 0.980 at Soft-404 pages.10aAlgorithms10aData mining10aDesign10aLink analysis10aPerformance10aSoft-404 error10aStatistical properties of the web10aWeb decay10aWeb spam1 aPrieto, V.M.1 aÁlvarez, M.1 aCacheda, F.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84903202899&partnerID=40&md5=fa1bd1e59e4da6c413c6527da17e6e6b01249nas a2200205   4500008004100000245006800041210006400109260000900173300001400182490000700196520059500203653001400798653001600812653002500828653002700853653002200880100001300902700001100915856011700926       2013                        eng d00aThe research of algorithm for data mining based on fuzzy theory0 aresearch of algorithm for data mining based on fuzzy theory  c2013  a327 - 3340 v113 aData Mining is a new filed in data processing research. Support Vector Machine (SVM) is one of the new methods using in data mining, which has gained great applicable success. However, there are still plenty of limitations in SVM. For example, SVM won't work if its training set contains uncertain information. In order to solve the problem presented above, this paper discusses the constraining programming of fuzzy chance and the characteristic of fuzzy classification as well as its expression methods. The algorithm for classifying Support Vector Machine is also included in this paper.10aAlgorithm10aData mining10aFuzzy classification10aFuzzy linear separable10aFuzzy programming1 aWang, A.1 aLi, J.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84890455119&partnerID=40&md5=cafd40536136512fd2e0485a0240a56502124nas a2200217   4500008004100000245006700041210006600108260000900174300001400183490000600197520142800203653002201631653002201653653001601675653003401691100001401725700001401739700002001753700001601773856011701789       2007                        eng d00aAnomaly detection through quasi-functional dependency analysis0 aAnomaly detection through quasifunctional dependency analysis  c2007  a191 - 2000 v53 aAnomaly detection problems have been investigated in several research areas such as database, machine learning, knowledge discovery, and logic programming, with the main goal of identifying objects of a given population whose behavior is anomalous with respect to a set of commonly accepted rules that are part of the knowledge base. In this paper we focus our attention on the analysis of anomaly detection in databases. We propose a method, based on data mining algorithms, which allows one to infer the "normal behavior" of objects, by extracting frequent "rules" from a given dataset. These rules are described in the form of quasi-functional dependencies and mined from the dataset by using association rules. Our approach allows us to consequently analyze anomalies with respect to the previously inferred dependencies: given a quasi-functional dependency, it is possible to discover the related anomalies by querying either the original database or the association rules previously stored. By further investigating the nature of such anomalies, we can either derive the presence of erroneous data or highlight novel information which represents significant exceptions of frequent rules. Our method is independent of the considered database and directly infers rules from the data. The applicability of the proposed approach is validated through a set of experiments on XML databases, whose results are here reported.10aAnomaly detection10aAssociation rules10aData mining10aQausi-functional dependencies1 aBruno, G.1 aGarza, P.1 aQuintarelli, E.1 aRossato, R.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-44949112157&partnerID=40&md5=cdbe91548ea650327ae0cdf87c4cf97f01850nas a2200193   4500008004100000245007600041210006900117260000900186300001400195490000600209520120400215653002101419653003101440653001901471653001601490653001701506100001601523856011701539       2006                        eng d00aPruning techniques in associative classification: Survey and comparison0 aPruning techniques in associative classification Survey and comp  c2006  a197 - 2020 v43 aAssociation rule discovery and classification are common data mining tasks. Integrating association rule and classification also known as associative classification is a promising approach that derives classifiers highly competitive with regards to accuracy to that of traditional classification approaches such as rule induction and decision trees. However, the size of the classifiers generated by associative classification is often large and therefore pruning becomes an essential task. In this paper, we survey different rule pruning methods used by current associative classification techniques. Further, we compare the effect of three pruning methods (database coverage, pessimistic error estimation, lazy pruning) on the accuracy rate and the number of rules derived from different classification data sets. Results obtained from experimenting on different data sets from UCI data collection indicate that lazy pruning algorithms may produce slightly higher predictive classifiers than those which utilise database coverage and pessimistic error pruning methods. However, the potential use of such classifiers is limited because they are difficult to understand and maintain by the end-user.10aAssociation rule10aAssociative classification10aClassification10aData mining10aRule pruning1 aThabtah, F.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-33749400880&partnerID=40&md5=0633368b3744eefc60e6e205c64d95f4