**5. Summary**

300 Bio-Inspired Computational Algorithms and Their Applications

algorithm suport degree average runtime

2. The analog data stream is the same as above. The running results of the algorithms are

algorithm suport degree average runtime

3. The analog data stream has three attributes. Each attribute has 20 possible values. The

algorithm suport degree average runtime

As shown in Table 1, with the support degree increasing, the frequent patterns of these two algorithms are rapidly reducing, the number of matching is reduced and eventually the runtime will be reduced. However, fp-tree algorithm not only needs to maintain the global frequent pattern tree, but also requires additional time to build a sub-pattern tree for each data segment. Then this algorithm saves the information of the sub-pattern tree to the global frequent pattern tree. With the times of process increasing,the runtime of fp-tree algorithm

Table 2 shows that, with the support degree increasing, the algorithms which use pattern tree to maintain the information of the frequent patterns such as Dstree algorithm can not

10% 20% 30% 10% 20% 30%

10% 20% 30% 10% 20% 30% 0.156 0.087 0.029 0.087 0.032 0.015

0.138 0.139 0.141 0.087 0.032 0.015

0.406 0.397 0.402 0.090 0.041 0.017

10% 20% 30% 10% 20% 30%

fp-tree fp-tree fp-tree NSWGA NSWGA NSWGA

Dstree Dstree Dstree NSWGA NSWGA NSWGA

shown in Table 2.

Table 1. The comparison of fp-tree **algorithm** and NSWGA algorithm

Table 2. The comparison 1 of Dstree algorithm and NSWGA algorithm

Table 3. The comparison 2 of Dstree algorithm and NSWGA algorithm

reduce the runtime, but NSWGA algorithm is able to save a lot of runtime.

running results of the algorithms are shown in Table 3.

Dstree Dstree Dstree NSWGA NSWGA NSWGA

**4.2 Analysis of the experimental results** 

is becoming longer than NSWGA.

It is important for prediction and decision-making to find frequent items among huge data stream. This chapter presents an approach, namely NSWGA (Nested Sliding Window Genetic Algorithm), about mining frequent itemsets on data stream within the current window. NSWGA uses the parallelism of genetic algorithm to search for the frequent itemset of the latest data in the nested sub-window. The final frequent itemsets of the sliding window is obtained by the integrated treatment of this series of frequent itemsets in nested sub-window. NSWGA captures the latest frequent itemsets accurately and timely on data stream. At the same time the expired data is deleted periodically. As the use of nested windows and the parallel processing capability of genetic algorithm, this method reduced the time complexity.

In this chapter, an algorithm about mining frequent patterns of data stream- NSWGA algorithm is proposed. The main contributions of this algorithm: (1) The parallelism of genetic algorithm is used to mine the frequent patterns of data stream , which reduces the runtime; (2) The algorithm combines the sliding window with genetic algorithm to propose an improved method to obtain initial population; (3) This algorithm gurantees the speed of implementation and query precision.
