01973nas a2200277 4500008004100000245008900041210006900130260001200199300001200211490000700223520116400230653001601394653003301410653003901443653002501482100001401507700001901521700001601540700001701556700001901573700001301592700001501605700001801620700002001638856003701658 2013 eng d00aMining Effective Multi-Segment Sliding Window for Pathogen Incidence Rate Prediction0 aMining Effective MultiSegment Sliding Window for Pathogen Incide c09/2013 a425-4440 v873 aPathogen incidence rate prediction, which can be considered as time series modeling, is an important task for infectious disease incidence rate prediction and for public health. This paper investigates applying a genetic computation technique, namely GEP, for pathogen incidence rate prediction. To overcome the shortcomings of traditional sliding windows in GEP based time series modeling, the paper introduces the problem of mining effective sliding window, for discovering optimal sliding windows for building accurate prediction models. To utilize the periodical characteristic of pathogen incidence rates, a multi-segment sliding window consisting of several segments from different periodical intervals is proposed and used. Since the number of such candidate windows is still very large, a heuristic method is designed for enumerating the candidate effective multi-segment sliding windows. Moreover, methods to find the optimal sliding window and then produce a mathematical model based on that window are proposed. A performance study on real-world datasets shows that the techniques are effective and efficient for pathogen incidence rate prediction.10aData Mining10aMulti-segment sliding window10aPathogen incidence rate prediction10aTime series modeling1 aDuan, Lei1 aTang, Changjie1 aLi, Xiasong1 aDong, Guozhu1 aWang, Xianming1 aZuo, Jie1 aJiang, Min1 aLi, Zhongqiao1 aZhang, Yongqing uhttp://www.knoesis.org/node/246201231nas a2200133 4500008004100000245004700041210004600088520085700134100001500991700001801006700001901024700001701043856003701060 2009 eng d00aMaintenance of Frequent Patterns: A Survey0 aMaintenance of Frequent Patterns A Survey3 aThis chapter surveys the maintenance of frequent patterns in transaction datasets. It is written to be accessible to researchers familiar with the field of frequent pattern mining. The frequent pattern main-tenance problem is summarized with a study on how the space of frequent patterns evolves in response to data updates. This chapter focuses on incremental and decremental maintenance. Four major types of maintenance algorithms are studied: Apriori-based, partition-based, prefix-tree-based, and concise-representation-based algorithms. The authors study the advantages and limitations of these algorithms from both the theoretical and experimental perspectives. Possible solutions to certain limitations are also proposed. In addition, some potential research opportunities and emerging trends in frequent pat-tern maintenance are also discussed.1 aLi, Jinyan1 aWong, Limsoon1 aFeng, Mengling1 aDong, Guozhu uhttp://www.knoesis.org/node/205702167nas a2200133 4500008004100000245004100041210004100082520180700123100001701930700001601947700001801963700001501981856003701996 2009 eng d00aMining Conditional Contrast Patterns0 aMining Conditional Contrast Patterns3 aThis chapter considers the problem of 'conditional contrast pattern mining.' It is related to contrast mining, where one considers the mining of patterns/models that contrast two or more datasets, classes, conditions, time periods, and so forth. Roughly speaking, conditional contrasts capture situations where a small change in patterns is associated with a big change in the matching data of the patterns. More precisely, a conditional contrast is a triple (B, F_{1}, F_{2}) of three patterns; B is the condition/context pattern of the conditional contrast, and F_{1} and F_{2} are the contrasting factors of the conditional contrast. Such a conditional contrast is of interest if the difference between F_{1} and F_{2} as itemsets is relatively small, and the difference between the corresponding matching dataset of B∪F_{1} and that of B∪F_{2 is relatively large. It offers insights on 'discriminating' patterns for a given condition B. Conditional contrast mining is related to frequent pattern mining and analysis in general, and to the mining and analysis of closed pattern and minimal generators in particular. It can also be viewed as a new direction for the analysis (and mining) of frequent patterns. After formalizing the concepts of conditional contrast, the chapter will provide some theoretical results on conditional contrast mining. These results (i) relate conditional contrasts with closed patterns and their minimal generators, (ii) provide a concise representation for conditional contrasts, and (iii) establish a so-called dominance-beam property. An efficient algorithm will be proposed based on these results, and experiment results will be reported. Related works will also be discussed.1 aDong, Guozhu1 aLiu, Guimei1 aWong, Limsoon1 aLi, Jinyan uhttp://www.knoesis.org/node/205802461nas a2200181 4500008004100000245007400041210006900115520189200184653003202076653001902108653002002127653002902147653001602176100001902192700001402211700001702225856003702242 2009 eng d00aMining Disease State Converters for Medical Intervention of Diseases.0 aMining Disease State Converters for Medical Intervention of Dise3 aIn applications such as gene therapy and drug design, a key goal is to convert the disease state of diseased objects from an undesirable state into a desirable one. Such conversions may be achieved by changing the values of some attributes of the objects. For example, in gene therapy one may convert cancerous cells to normal ones by changing some genes' expression level from low to high or from high to low. In this paper, we define the disease state conversion problem as the discovery of disease state converters; a disease state converter is a small set of attribute value changes that may change an object's disease state from undesirable into desirable. We consider two variants of this problem: personalized disease state converter mining mines disease state converters for a given individual patient with a given disease, and universal disease state converter mining mines disease state converters for all samples with a given disease. We propose a DSCMiner algorithm to discover small and highly effective disease state converters. Since real-life medical experiments on living diseased instances are expensive and time consuming, we use classifiers trained from the datasets of given diseases to evaluate the quality of discovered converter sets. The effectiveness of a disease state converter is measured by the percentage of objects that are successfully converted from undesirable state into desirable state as deemed by state-of-the-art classifiers. We use experiments to evaluate the effectiveness of our algorithm and to show its effectiveness. We also discuss possible research directions for extensions and improvements. We note that the disease state conversion problem also has applications in customer retention, criminal rehabilitation, and company turn-around, where the goal is to convert class membership of objects whose class is an undesirable class.10aClass membership conversion10aClassification10aContrast mining10aDisease state conversion10aDrug design1 aTang, Changjie1 aDuan, Lei1 aDong, Guozhu uhttp://www.knoesis.org/node/144600373nas a2200121 4500008004100000245005300041210005300094100001700147700002100164700001500185700001400200856003700214 2008 eng d00aMining Sequence Classifiers for Early Prediction0 aMining Sequence Classifiers for Early Prediction1 aDong, Guozhu1 aXing, Zhengzheng1 aYu, Philip1 aPei, Jian uhttp://www.knoesis.org/node/107900384nas a2200109 4500008004100000245007600041210006900117100001700186700001600203700001800219856003700237 2007 eng d00aMining Minimal Distinguishing Subsequence Patterns with Gap Constraints0 aMining Minimal Distinguishing Subsequence Patterns with Gap Cons1 aDong, Guozhu1 aJi, Xiaonan1 aBailey, James uhttp://www.knoesis.org/node/149000484nas a2200109 4500008004100000245010900041210006900150260008500219100001600304700001700320856003700337 2006 eng d00aMasquerader Detection Using OCLEP: One-Class Classification Using Length Statistics of Emerging Patterns0 aMasquerader Detection Using OCLEP OneClass Classification Using bInternational Workshop on INformation Processing over Evolving Networks (WINPEN)1 aChen, Lijun1 aDong, Guozhu uhttp://www.knoesis.org/node/181400449nas a2200133 4500008004100000245009300041210006900134100001800203700001500221700001700236700001100253700001400264856003700278 2006 eng d00aMinimum Description Length (MDL) Principle: Generators Are Preferable to Closed Patterns0 aMinimum Description Length MDL Principle Generators Are Preferab1 aWong, Limsoon1 aLi, Jinyan1 aDong, Guozhu1 aLi, H.1 aPei, Jian uhttp://www.knoesis.org/node/110500436nas a2200133 4500008004100000245007000041210006800111100001900179700001600198700001700214700001800231700001600249856003700265 2002 eng d00aMulti-Dimensional Regression Analysis of Time-Series Data Streams0 aMultiDimensional Regression Analysis of TimeSeries Data Streams1 aWang, Jianyong1 aHan, Jiawei1 aDong, Guozhu1 aWah, Benjamin1 aChen, Yixin uhttp://www.knoesis.org/node/107800459nas a2200145 4500008004100000245007000041210006800111100001400179700001900193700001800212700001600230700001300246700001700259856003700276 2002 eng d00aMultiDimensional Regression Analysis of Time-Series Data Streams.0 aMultiDimensional Regression Analysis of TimeSeries Data Streams1 aPei, Jian1 aWang, Jianyong1 aWah, Benjamin1 aHan, Jiawei1 aZou, Wei1 aDong, Guozhu uhttp://www.knoesis.org/node/111300400nas a2200109 4500008004100000245008300041210006900124100002800193700001700221700001500238856003700253 2001 eng d00aMaking Use of the Most Expressive Jumping Emerging Patterns for Classification0 aMaking Use of the Most Expressive Jumping Emerging Patterns for 1 aRamamohanarao, Kotagiri1 aDong, Guozhu1 aLi, Jinyan uhttp://www.knoesis.org/node/154600417nas a2200133 4500008004100000245006600041210006400107100001500171700001300186700001400199700001700213700001600230856003700246 2001 eng d00aMining Multi-Dimensional Constrained Gradients in Data Cubes.0 aMining MultiDimensional Constrained Gradients in Data Cubes1 aLam, Joyce1 aWang, Ke1 aPei, Jian1 aDong, Guozhu1 aHan, Jiawei uhttp://www.knoesis.org/node/154800400nas a2200109 4500008004100000245008300041210006900124100001700193700001500210700002800225856003700253 2000 eng d00aMaking Use of the Most Expressive Jumping Emerging Patterns for Classification0 aMaking Use of the Most Expressive Jumping Emerging Patterns for 1 aDong, Guozhu1 aLi, Jinyan1 aRamamohanarao, Kotagiri uhttp://www.knoesis.org/node/154100374nas a2200121 4500008004100000245005200041210005200093100001900145700001600164700001700180700001800197856003700215 1999 eng d00aMaintaining Transitive Closure of Graphs in SQL0 aMaintaining Transitive Closure of Graphs in SQL1 aLibkin, Leonid1 aSu, Jianwen1 aDong, Guozhu1 aWong, Limsoon uhttp://www.knoesis.org/node/153400360nas a2200097 4500008004100000245007000041210006900111100002800180700001700208856003700225 1997 eng d00aMaintaining constrained transitive closure by conjunctive queries0 aMaintaining constrained transitive closure by conjunctive querie1 aKotagiri, Ramamohanarao1 aDong, Guozhu uhttp://www.knoesis.org/node/150900288nas a2200085 4500008004100000245005800041210004900099100001700148856003700165 1993 eng d00aOn the monotonicity of (LDL) logic programs with sets0 amonotonicity of LDL logic programs with sets1 aDong, Guozhu uhttp://www.knoesis.org/node/1531}