Structure analysis) [31?3] combines the random surf model of PageRank with hub

Structure analysis) [31?3] combines the random surf model of PageRank with hub/authority principle of HITS. It generates a bipartite undirected graph H based on the web graph G. One subset of H contains all the nodes with positive in-degree (the potential “authorities”) and the other subset consists of all the nodes with positive out-degree (the potential “hubs”). A travel is completed by a two-step random walk. For example, from the “hub” to the “authority” and from the “authority” back to the “hub”. As in the PageRank, each individual walk is a Markov process with a well-defined transition probability matrix [31]. Nevertheless, besides SALAS does not really implement the “FCCP mutual reinforcement” of HITS because the scores of both authority and hub are not related by the hub to authority and authority to hub reinforcement operations, its score propagation differs from HITS (a similarity-mediated score propagation). Moreover, its random walk model does not directly simulate the behavior of the surfer in PageRank either. For SALAS, a surfer can jump from webpage pi to pj even though there is no hyperlink between them, and there is no link-interrupt jumps. Based on a similar approach as SALAS, Ding et al proposed a unified framework integrating HITS and PageRank [34]. Figure 1 indicates that a database can be represented by a bipartite graph equally [25]. In the graph, left is the table layout representation and can be represented by the bipartite graph on the right. Compounds and features linked to each other can be viewed as webpages. As a consequence, the link-based algorithms used to rank the webpage such as HITS or PageRank can be utilized to rank compounds or features. The algorithms say that if a webpage has many important links to it, the links from it to otherMining by Link-Based Associative Classifier (LAC)webpages become important too. For our case, this means a highly weighted compound should contain many highly weighted features and a highly weighted feature should exist in many highly weighted compounds. Accordingly, the ranking score can be used for feature weighting. Although Ding’s unified framework can be used to derive the ranking score automatically, it SC 1 cannot distinguish the contributions of different types of connections. For chemical dataset mining, each chemical feature may connect to both active and inactive compounds; for biological dataset mining, each gene may connect to a disease either as suppressor or activator. Chemical features existing frequently in active compounds or genes major associated with suppressors are more interested in. In Figure 1, when we consider the contribution of compounds to the weight of a node/attribute 78, we want to distinguish the contribution of compound 5469540 from the contribution of compound 840827 and 5911714. Ding’s unified framework treats the contribution of the nodes equally as a homogenous system [34]; Chen et al developed a framework calculating the weight for either homogenous or heterogeneous systems [35]. In Chen’s model, connections can have different impacts on a node. In this paper, we describe a link-based unified weighting framework which combines the mutual reinforcement of HITS with hyperlink weighting normalization of PageRank based on Ding and Chen’s frameworks, resulting in highly efficient linkbased weighted associative classifier mining from biomedical 24272870 datasets without pre-assigned weight information. Our main contributions are: 1) developmen.Structure analysis) [31?3] combines the random surf model of PageRank with hub/authority principle of HITS. It generates a bipartite undirected graph H based on the web graph G. One subset of H contains all the nodes with positive in-degree (the potential “authorities”) and the other subset consists of all the nodes with positive out-degree (the potential “hubs”). A travel is completed by a two-step random walk. For example, from the “hub” to the “authority” and from the “authority” back to the “hub”. As in the PageRank, each individual walk is a Markov process with a well-defined transition probability matrix [31]. Nevertheless, besides SALAS does not really implement the “mutual reinforcement” of HITS because the scores of both authority and hub are not related by the hub to authority and authority to hub reinforcement operations, its score propagation differs from HITS (a similarity-mediated score propagation). Moreover, its random walk model does not directly simulate the behavior of the surfer in PageRank either. For SALAS, a surfer can jump from webpage pi to pj even though there is no hyperlink between them, and there is no link-interrupt jumps. Based on a similar approach as SALAS, Ding et al proposed a unified framework integrating HITS and PageRank [34]. Figure 1 indicates that a database can be represented by a bipartite graph equally [25]. In the graph, left is the table layout representation and can be represented by the bipartite graph on the right. Compounds and features linked to each other can be viewed as webpages. As a consequence, the link-based algorithms used to rank the webpage such as HITS or PageRank can be utilized to rank compounds or features. The algorithms say that if a webpage has many important links to it, the links from it to otherMining by Link-Based Associative Classifier (LAC)webpages become important too. For our case, this means a highly weighted compound should contain many highly weighted features and a highly weighted feature should exist in many highly weighted compounds. Accordingly, the ranking score can be used for feature weighting. Although Ding’s unified framework can be used to derive the ranking score automatically, it cannot distinguish the contributions of different types of connections. For chemical dataset mining, each chemical feature may connect to both active and inactive compounds; for biological dataset mining, each gene may connect to a disease either as suppressor or activator. Chemical features existing frequently in active compounds or genes major associated with suppressors are more interested in. In Figure 1, when we consider the contribution of compounds to the weight of a node/attribute 78, we want to distinguish the contribution of compound 5469540 from the contribution of compound 840827 and 5911714. Ding’s unified framework treats the contribution of the nodes equally as a homogenous system [34]; Chen et al developed a framework calculating the weight for either homogenous or heterogeneous systems [35]. In Chen’s model, connections can have different impacts on a node. In this paper, we describe a link-based unified weighting framework which combines the mutual reinforcement of HITS with hyperlink weighting normalization of PageRank based on Ding and Chen’s frameworks, resulting in highly efficient linkbased weighted associative classifier mining from biomedical 24272870 datasets without pre-assigned weight information. Our main contributions are: 1) developmen.