High utility itemset mining is an interesting research in the field of data mining, which can find more valuable information than frequent itemset mining. Several high-utility itemset mining approaches have already been proposed; however, they have high computational costs and low efficiency. To solve this problem, a high-utility itemset mining algorithm based on the particle filter is proposed. This approach first initializes a population, which consists of particle sets. Then, to update the particle sets and their weights, a novel state transition model is suggested. Finally, the approach alleviates the particle degradation problem by resampling. Substantial experiments on the UCI datasets show that the proposed algorithm outperforms the other previous algorithms in terms of efficiency, the number of high-utility itemsets, and convergence.
With the improvement of System-on-Chip integration, the chip requires an increasingly large amount of test data. To solve the contradiction between the storage capacity and bandwidth of automatic test equipment (ATE), a new method of test data compression/decompression is proposed based on an annular scan chain. Corresponding fault bits of different test patterns are incompatible, moving test patterns in an annular scan chain, makes all of the new corresponding bits of different test patterns be compatible or backward-compatible, so different adjacent test patterns form a new relation that are indirectly compatible or indirectly backward-compatible, achieves the purpose of test data compression by encoding these indirectly compatible test patterns or indirectly backward-compatible test patterns. According to experimental results, the average compression ratio increases by %6.94 to % 15.1 compared with the other schemes, relative decompression architecture is simple. In the annular scan chain, the test pattern moves clockwise with the minimal bits, generating subsequent test patterns quickly, it is advantageous to reduce the test application time of a single IP core.
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