Mining Frequent Sets Using Fuzzy Multiple-Level Association Rules

At present, most of the association rules algorithms are based on the Boolean attribute and single-level association rules mining. But data of the real world has various types, the multi-level and quantitative attributes are got more and more attention. And the most important step is to mine frequent sets. In this paper, we propose an algorithm that is called fuzzy multiple-level association （FMA） rules to mine frequent sets. It is based on the improved Eclat algorithm that is different to many researchers’ proposed algorithms thatused the Apriori algorithm. We analyze quantitative data’s frequent sets by using the fuzzy theory, dividing the hierarchy of concept and softening the boundary of attributes’ values and frequency. In this paper, we use the vertical-style data and the improved Eclat algorithm to describe the proposed method, we use this algorithm to analyze the data of Beijing logistics route. Experiments show that the algorithm has a good performance, it has better effectiveness and high efficiency.

Automatic Generation Control Based on Multiple-step Greedy Attribute and Multiple-level Allocation Strategy

The strong stochastic disturbance caused by largescale distributed energy access to power grids affects the security,stability and economic operations of the power grid.A novel multiple-step greedy policy based on the consensus Qlearning(MSGP-CQ)strategy is proposed in this paper,which is an automatic generation control(AGC)for distributed energy incorporating multiple-step greedy attribute and multiple-level allocation strategy.The convergence speed and learning efficiency in the MSGP algorithm are accelerated through the predictive multiple-step iteration updating in the proposed strategy,and the CQ algorithm is adopted with collaborative consensus and selflearning characteristics to enhance the adaptability of the power allocation strategy under the strong stochastic disturbances and obtain the total power commands in the power grid and the dynamic optimal allocations of the unit power.The simulations of the improved IEEE two-area load-frequency control(LFC)power system and the interconnected system model of intelligent distribution network(IDN)groups incorporating a large amount of distributed energy show that the proposed strategy can achieve the optimal coordinated control and power allocation in the power grid.The algorithm MSGP-CQ has stronger robustness and faster dynamic optimization speed and can reduce generation costs.Meanwhile it can also solve the strong stochastic disturbance caused by large-scale distributed energy access to the grid compared with some existing intelligent algorithms.

Recognizing Visual Attention Allocation Patterns Using Gaze Behaviors

Human Attention Allocation Strategy (HAAS) is related closely to operating performance when he/she is interacting a machine through a human-machine interface. Gaze behaviors, which is acquisited by eye tracking technology, can be used to observe attention allocation. But the performance-sensitive attention allocation strategy is still hard to measure using gaze cue. In this paper, we attempt to understand visual attention allocation behavior and reveal the relationship between attention allocation strategy and interactive performance in a quantitative manner. By using a novel Multiple-Level Clustering approach, we give some results on probabilistic analysis about interactive performance of HAAS patterns in a simulation platform of thermal-hydraulic process plant. It can be observed that these patterns are sensitive to interactive performance. We conclude that our Multiple-Level Clustering approach can extract efficiently human attention allocation patterns and evaluate interactive performance using gaze movements.