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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.

We present a time-domain transmit beamforming (TDTB) method for self-interference cancelation (SIC) at the radio frequency (RF) frontend of the receivers on broadband full-duplex MIMO radios. It is shown that the conventional frequency-domain transmit beamforming (FDTB) method along with the orthogonal frequency division multiplexing (OFDM) framework does not generally perform SIC in the prefix region of a transmitted frame. A hardware based test of the TDTB method shows a 50dB SIC over a bandwidth of 30MHz.

Existing classification algorithms in machine learning mainly use heuristic search to find a subset of regularities in data for classification. In the past few years, extensive research was done in the database community on learning rules using exhaustive search under the name of association rule mining. Although the whole set of rules may not be used directly for accurate classification, effective classifiers have been built using the rules. This paper aims to improve such an exhaustive search based classification system CBA (Classification Based on Associations). The main strength of this system is that it is able to use the most accurate rules for classification. However, it also has weaknesses. This paper proposes two new techniques to deal with these weaknesses. This results in remarkably accurate classifiers. Experiments on a set of 34 benchmark datasets show that on average the new techniques reduce the error of CBA by 17% and is superior to CBA on 26 of the 34 datasets. They reduce the error of C4.5 by 19%, and improve performance on 29 datasets. Similar good results are also achieved against RIPPER, LB and a Naïve-Bayes classifier.