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Association rules are a fundamental class of patterns that exist in data. The key strength of association rule mining is its completeness. It finds all associations in the data that satisfy the user specified minimum support and minimum confidence constraints. This strength, however, comes with a major drawback. It often produces a huge number of associations. This ~ is particularly true for data sets whose attributes are highly correlated. The huge number of associations makes it very difficult, if not impossible, for a human user to analyze in order to identify those interesting/useful ones. In this paper, we propose a novel technique to overcome this problem. The technique first prunes the discovered associations to remove those insignificant associations, and then finds a special subset of the unpruned associations to form a summary of the discovered associations.We call this subset of associations the direction setting (DS) rules as they set the directions that are followed by the rest of the associations. Using this summary, the user can focus on the essential aspects (or relationships) of the domain and selectively view the relevant details. The approach is effective because experiment results show that the set of DS rules is typically very small. They can be analyzed manually by a human user. The proposed technique has also been applied successfully to a number of real-life applications.

Abstract-Online reviews provide valuable information about products and services to consumers. However, spammers are joining the community trying to mislead readers by writing fake reviews. Previous attempts for spammer detection used reviewers' behaviors, text similarity, linguistics features and rating patterns. Those studies are able to identify certain types of spammers, e.g., those who post many similar reviews about one target entity. However, in reality, there are other kinds of spammers who can manipulate their behaviors to act just like genuine reviewers, and thus cannot be detected by the available techniques. In this paper, we propose a novel concept of a heterogeneous review graph to capture the relationships among reviewers, reviews and stores that the reviewers have reviewed. We explore how interactions between nodes in this graph can reveal the cause of spam and propose an iterative model to identify suspicious reviewers. This is the first time such intricate relationships have been identified for review spam detection. We also develop an effective computation method to quantify the trustiness of reviewers, the honesty of reviews, and the reliability of stores. Different from existing approaches, we don't use review text information. Our model is thus complementary to existing approaches and able to find more difficult and subtle spamming activities, which are agreed upon by human judges after they evaluate our results. 1

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A nano-Fe 3 O 4 -CoO x catalyst was prepared via a simple wet impregnation method. The nano-Fe 3 O 4 -CoO x catalyst showed good catalytic performance for the conversion of 5-hydroxymethylfurfural into 2, 5-furandicarboxylic acid (FDCA) with t-BuOOH as the oxidant. Several important reaction parameters were explored, with the highest FDCA yield of 68.6% obtained from HMF after 15 h at a reaction temperature of 80 o C. One-pot conversion of fructose into FDCA was also successful via two steps. Catalytic conversion of fructose over Fe 3 O 4 @SiO 2 -SO 3 H yielded 93.1% HMF, which was oxidized in-situ into FDCA with a yield of 59.8%. Furthermore, recycling of nano-Fe 3 O 4 -CoO x was accomplished with the help of a magnetic field. Nano-Fe 3 O 4 -CoO x showed high stability in the reaction process. The use of non-precious metals and no requirement of a base additive made this method much more economical and environmental-friendly.