der Bremsscheiben-Oberfläche — die sogenannten Hotspots —, welche das is experimental and the keywords may be updated as the learning algorithm. allocation and scheduling in dense mobile access networks. › 10 Backhaul Network Reconfiguration Extension for DenseNets. › Hotspot BFS Algorithm. This paper, therefore, presents the Subnet Based Hotspot Algorithm (SBHA) that not only discus the strategy of network division in the form of subnets but also.
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This results in network partitioning and this problem well known hot spot or energy hole problem. In this paper, a Gravitational Search Algorithm (GSA) approach. This paper, therefore, presents the Subnet Based Hotspot Algorithm (SBHA) that not only discus the strategy of network division in the form of subnets but also. Definition: Was ist "Hot Spot"? Teilen Sie Ihr Wissen über "Hot Spot" the greedy strategy is used to improve the density clustering algorithm, which can. Bibliographic details on An Algorithm for Alleviating the Effect of Hotspot on Throughput in Wireless Sensor Networks. Die Authentication Algorithm Identification (AAI) ist eine Spezifikation von WLANs Die Möglichkeit, dass der Access Point die WLAN-Station authentifiziert. Now you can enable Portable Wifi HotSpot and the Bluetooth so easily and quickly with this applicaiton. You can also turn mobile data on or off using this. allocation and scheduling in dense mobile access networks. › 10 Backhaul Network Reconfiguration Extension for DenseNets. › Hotspot BFS Algorithm.
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General association rule algorithms can only process discrete data, such as classical Apriori algorithm [ 2 ].
If real-type data is to be processed, it needs to be discretized, and in practical applications, most of the data in reality is real.
The HotSpot algorithm selected in this paper can directly mine association rules and dynamically acquire the range of real number intervals without discretization of real data, thus avoiding the influence of subjective factors in discretization, and the processing speed is extremely fast.
For example, a dataset of , records with 45 discrete-valued fields can be processed in 10 seconds. But like Apriori algorithm, HotSpot algorithm has one obvious shortcoming: support selection needs to be set artificially based on experience and cannot be set accurately according to the actual scale of the problem [ 3 ].
If the support threshold is set too low, a cumbersome and complicated tree structure may be generated; if the support threshold is set too high, some associated intervals existing in the rare target attribute values may be ignored.
Therefore, in the process of support selection, multiple comparison experiments are needed to determine the optimal support based on the mining results.
In order to overcome the sensitivity of HotSpot algorithm in support threshold settings and improve the quality of association rule mining, this paper combines intelligent optimization technology with association rule-mining technology.
With the help of the good global search ability of simulated annealing algorithm, support can be set by machine learning, so as to solve the problem that unreasonable threshold setting affects the effect of association rules mining.
There have been plenty of important research studies in the data mining field. Li uses the HotSpot algorithm to perform handover event association rule mining [ 4 ]; Wang researched through HotSpot algorithm the associate degree between customer and evaluation index of express train service quality [ 5 ].
Wang presented an improved K -means algorithm by combining the agglomerative hierarchical clustering algorithm to select the initial cluster centers for HotSpot discovery in Internet public opinions [ 6 ].
Mallik et al. Sitanggang and Fatayati used the SPADE algorithm to generate sequential patterns on hotspot datasets in Sumatra Island, Indonesia, in and and then used association rule mining to obtain association between the locations of hotspot sequences and weather conditions [ 8 ].
Liu proposed a model by using the traditional vector space model VSM , K -means algorithm, and SVM classifier, for Internet public opinion hotspot detection and analysis [ 9 ].
Di Martino et al. Nisa et al. Agrawal and Choudhary used lung cancer data from SEER dataset with 13 predictor attributes for association rule-mining analysis [ 12 ].
Zhang et al. While association rule mining is a core problem of data mining, the efficiency of mining algorithms is influenced by the data size.
When there are very long patterns present in the data, it is often impractical to generate the entire set of frequent itemsets or closed itemsets.
The set of maximal frequent itemsets MFI contains all frequent sets [ 14 — 17 ]. The MFI is the smallest possible representation of the data that can still be used to generate the frequent itemsets which is way bigger than the number of MFI.
Once the frequent itemset is generated, the support information can be easily recomputed from the transactional database.
Definition 1. The minimum number of segments is used to determine whether there is enough data to segment real number intervals: where means the minimum support and means the total number of samples in the dataset.
Definition 2. The maximum confidence level is used to determine the optimal segmentation point and reflects the reliability of the rules: where and , respectively, represent the number of transactions that meet the target value on both sides of the traversal point, and and , respectively, represent the number of transactions on both sides of the traversal point.
The and can be add-self obtained by judging the value of the target attribute by each traversal. The , , , and always satisfy formulas 3 and 4 during the traversal: where means that the dataset meets the number of target attribute values.
Definition 3. The maximum number of branches limits the number of branches of the tree structure during the construction of the child node.
Definition 4. The target attribute support:. Definition 5. The minimum improvement, let be a itemset and add an itemset to to make it.
Namely, means itemset. At this point, the improvement of is improved by , if , and the itemset is added to the tree branch. This indicates that the addition of this set contributes to the relevance between and :.
HotSpot algorithm is an association rule-mining algorithm based on the tree structure [ 4 ], and it can mine discrete datasets or directly mine real data.
Namely, the continuous data is divided by finding the associated interval, which is convenient for people to understand and analyze the relationship between the target attribute column and the associated attribute [ 18 ].
HotSpot algorithm finds the association interval that satisfies the minimum support, and the mining result is presented in a tree structure [ 19 , 20 ].
When the given association attribute is continuous data, the specific steps of the algorithm are as follows: 1 statistical support of target attribute column, judging whether it is greater than the minimum support and taking the target attribute value as the root node.
Calculate the confidence of each value by formula 2 and record the new on the premise of increasing the confidence; otherwise, calculate the of the next value, and finally determine the best segmentation point by.
If , it is regarded as the child node joining candidate queue, and the segmentation point is recorded to output the HotSpot association rule tree.
The HotSpot algorithm for the mining of association rules depends on the artificially based minimum support and cannot be accurately set according to the actual scale of the dataset.
This may lead to the following problems in the execution of the algorithm: 1 If support is set too low, a cumbersome and complicated tree structure may occur, which makes the association rules too much and is difficult to extract.
In addition, it is easy to result in fewer samples satisfying the range of association attributes, which makes association rules meaningless.
The idea of simulated annealing algorithm was proposed by Metropolis et al. At present, it has been widely used in production scheduling, artificial intelligence, and other fields.
The simulated annealing algorithm starts to cool down from a higher initial temperature and combines the probability jump feature in the annealing process to randomly search for the global optimal solution in the solution space.
The specific ideas are as follows: 1 Determine the range of the model solution space, randomly generate an initial solution in the solution space, and calculate the corresponding objective function value 2 Set the initial temperature 3 Perturbation according to the current solution , generates a new solution , and calculating the corresponding objective function value to obtain 4 Determine whether to accept the new solution according to the Metropolis criterion for the incremental value 5 In the homogeneous algorithm, steps 3 and 4 are repeated times at temperature ; in the nonhomogeneous algorithm, this step is ignored 6 Annealing according to the temperature update function, namely, let be equal to the next value in the annealing schedule 7 Repeat steps 3 — 6 until the termination condition are met.
Finally, the support threshold is set by machine learning and combined with the mining background so that the quality of the mining association rules is improved and the quantity is reasonable, and the mining effect of the association rules is optimized.
The key to the improvement of HotSpot algorithm lies in the setting of energy function E , state generation function, and temperature update function.
The idea of setting these functions is emphasized below. There are two main factors influencing the results of HotSpot algorithm mining: 1 confidence: Conf; 2 cover: the number of samples satisfying the range of association attributes in each association rule.
Therefore, this paper uses the average confidence of all association rules to measure the importance of mining results while constructing the energy function, which is called the importance factor ; the sample coverage is measured by the ratio of the number of samples in all association rules that satisfy the scope of the associated attribute the number of association rules Card x , called the sample coverage factor.
In view of this, this paper constructs an energy function model by Euclidean distance formula. As shown in Figure 1 , where is the ideal point, namely, , A is the value of and under current support and improvement.
The energy function is the distance from point A to point O, see formula 7. From formula 7, means the number of association rules and means the number of samples in each association rule that satisfy the range of association attributes:.
The essential function of the state generation function is to make the candidate values of the support as far as possible throughout the solution space 0 to 1 so that the selection of the support is reasonable and global.
After many comparative experiments, this paper makes and. Because of the law of temperature change in the process of exponential annealing, the annealing rate is only related to the value of , so it is a common annealing strategy.
A better exponential anneal function is shown in formula 9 , where is an empirical value, which is a constant slightly less than 1, generally in the range of [0.
After many comparative experiments, this paper makes :. In this process, the support threshold is set. It can find by itself through machine learning, and there is no need to manually select the support threshold several times.
The time complexity of the algorithm is , where is the number of attribute indexes and is the number of samples.
Software configuration includes Windows 8. After data preprocessing such as data integration, data dimensionality reduction, and screening of Inner Mongolia sites [ 23 ], the association rule-mining objects are constructed in the form shown in Figure 3.
In the mining process, the sandstorm grade is defined as the target attribute, the maximum number of branches is 2, the minimum improvement degree is 0.
BS and AS represent the number of association rules before and after filtering according to the index of association rules, respectively.
For example, when the support is 0. The small denominator causes the occurrence of the meteorological factor range itself to be a small probability event, which is not representative.
Such an association rule has little meaning. Therefore, the rules mined by the HotSpot algorithm are not as important as the higher confidence level, and it is also necessary to make the number of samples satisfying the range of meteorological factors not too low.
Therefore, this paper conducts the meteorological factor range coverage test and confidence test for the association rules of HotSpot mining so that the mining association rules are reliable.
On this basis, the association rules are screened as follows: i Meteorological factor range coverage test: keep the association rule of meteorological factor range coverage :.
From Table 1 , it can be found that the HotSpot algorithm runs extremely fast, with an average running time of less than half a second.
The total number of association rules after screening through subjective evaluation indicators and objective evaluation indicators:.
In addition, it can be found that when the target attribute is listed as I4 and the support is 0. Similarly, 0. When the target attribute is listed as I1 and the support is as low as 0.
In Table 2 , in terms of the number of association rules, the total number of association rules after screening through subjective evaluation indicators and objective evaluation indicators is.
In addition, the optimal support interval indicates that, in this interval, the value of energy function is the same and the minimum, namely, the mining results are the same in this interval.
It can be found that when the target attribute column is I4, the optimal support interval is about 0. The results are basically consistent with those of Section 6.
In terms of the quality of association rules, take the target attribute I3 as an example for comparison experiments.
From Figure 5 , it can be found that the probability of I3 sandstorm is At this time, the optimal support interval is It can be seen from Section 6.
The mining result of the HotSpot algorithm is shown in Figure 6. Figure 6 has three association rules. The meteorological factor coverage of the three rules is calculated according to equation 10 : In summary, the performance comparison is shown in Table 3.
Although it takes less time, it needs several comparative experiments to determine the optimal support according to the mining results.
The paired t -test is used to compare the values of means from two related samples. The value is set to 0. Figure 7 shows the test results of both BS and AS dataset.
It can be seen from the table- paired samples statistics in Figure 7 that the average number of association rules after filtering using the HotSpot algorithm is 2.
Many routing algorithms have been proposed to improve the performance of NoC. Some routing algorithms only have superiority under a specific traffic pattern, but they can have poor performance under other traffic patterns.
Compared to uniform traffic, some complex hotspot patterns are closer to reality. Traffic-aware routing algorithms are designed to solve this problem.
These traffic-aware routing algorithms commonly utilize virtual channels VC or routing tables to predict the future traffic distribution, which will have large power and hardware overheads that cannot be ignored.
To solve these problems, a VC-free traffic-pattern-aware routing algorithm based on West-first routing and North-last routing is proposed in this paper.
This algorithm contains a hotspot node and hotspot pattern detecting mechanism, which were designed to improve the performance of NoCs under different traffic patterns.
A hotspot information block which has a small cost is connected to each router to deal with the hotspot information and detect the hotspot patterns.
The simulation results show that routing algorithm proposed combines the advantages of the two existing routing algorithms and has better performance when considering different traffic patterns.