Frequent patterns can be partitioned into subsets according to F-list • F-list = f-c-a-b-m-p • Patterns containing p • Patterns having m but no p • … • Patterns having c but no a nor b, m, p • Pattern f • Completeness and non-redundancy翻译解释
时间: 2024-04-01 10:32:13 浏览: 25
频繁模式可以根据F-list划分为不同的子集。F-list是按照支持度从高到低排序的频繁模式列表。例如,如果F-list是f-c-a-b-m-p,那么我们可以将频繁模式划分为如下子集:
1. 包含p的频繁模式
2. 同时包含m但不包含p的频繁模式
3. 只包含c但不包含a、b、m、p的频繁模式
4. 单独的频繁模式f
在划分出这些子集后,我们可以对每个子集进行进一步的处理,例如,对每个子集中的频繁模式进行关联规则挖掘等。
另外,F-list的一个重要特性是它可以用来保证频繁模式的完整性和非冗余性。如果一个频繁模式被包含在F-list中,则它一定是频繁的,并且它不会被其他频繁模式所覆盖或包含。这样可以避免在挖掘频繁模式时产生重复或遗漏的情况。
相关问题
FPGrowth: A Frequent Pattern-Growth Approach翻译解释成中文
FPGrowth: A Frequent Pattern-Growth Approach是一篇关于频繁模式挖掘的论文。该论文提出了一种名为FPGrowth的频繁模式增长算法,用于高效地挖掘大规模数据中的频繁模式。
FPGrowth算法是一种非常常用的频繁模式挖掘算法,它的核心思想是利用FP树来存储数据,并通过递归生长的方式来挖掘频繁模式。与Apriori算法相比,FPGrowth算法具有更高的效率和更好的可扩展性。
该论文的研究成果对于数据挖掘领域具有很大的意义,可以为实际应用提供更高效、更准确的频繁模式挖掘算法。
python object cannot be re-sized
This statement is not entirely true. In Python, some objects are immutable, which means they cannot be resized or modified once they are created. Examples of immutable objects include integers, floats, tuples, and strings. On the other hand, some objects are mutable, which means they can be resized or modified after they are created. Examples of mutable objects include lists, dictionaries, and sets.
However, it is important to note that even mutable objects have some limitations on their resizing capabilities. For example, if a list is resized frequently, it can lead to performance issues due to the need for frequent memory reallocations. Therefore, it is important to consider the trade-offs between mutable and immutable objects when designing a program.