不精确数据下频繁渐进项集的发现
Discovering Frequent Gradual Itemsets with Imprecise Data
摘要：
对“X越多/越少，Y越多/越少”形式的属性的复杂共变进行建模的渐进模式在许多现实世界的应用中起着至关重要的作用，其中要管理的数值数据量很重要，这就是生物数据。最近，这些类型的模式已经引起了数据采掘社区的注意，其中已经定义了几种方法来从不同的数据模型中自动提取和管理这些模式。然而，这些方法往往面临着管理挖掘模式数量的问题，并且在许多实际应用中，由于用户定义的频率阈值，所有这些模式的计算可能被证明是棘手的，并且缺乏焦点导致产生巨大的模式集合。此外，传统方法的另一个问题是，渐进性的概念被定义为增加或减少。事实上，只要两个对象上的属性值不同，就认为是渐进性。结果，可以向用户呈现由传统算法提取的大量模式，尽管它们的渐进性只是数据中的噪声效应。为了解决这个问题，本文建议引入渐进性阈值，从该阈值考虑增加或减少。与文献方法相比，该方法考虑了属性值的分布，以及用户对渐进性阈值的偏好，并使得在某些文献方法由于搜索空间过大而失败的数据库上提取渐进模式成为可能。此外，从真实数据库的实验评估结果表明，该算法是可扩展的，高效的，可以消除大量的模式，不验证特定的渐进性要求，显示一个小的模式集给用户。
英文摘要：
The gradual patterns that model the complex co-variations of attributes of the form "The more/less X, The more/less Y" play a crucial role in many real world applications where the amount of numerical data to manage is important, this is the biological data. Recently, these types of patterns have caught the attention of the data mining community, where several methods have been defined to automatically extract and manage these patterns from different data models. However, these methods are often faced the problem of managing the quantity of mined patterns, and in many practical applications, the calculation of all these patterns can prove to be intractable for the user-defined frequency threshold and the lack of focus leads to generating huge collections of patterns. Moreover another problem with the traditional approaches is that the concept of gradualness is defined just as an increase or a decrease. Indeed, a gradualness is considered as soon as the values of the attribute on both objects are different. As a result, numerous quantities of patterns extracted by traditional algorithms can be presented to the user although their gradualness is only a noise effect in the data. To address this issue, this paper suggests to introduce the gradualness thresholds from which to consider an increase or a decrease. In contrast to literature approaches, the proposed approach takes into account the distribution of attribute values, as well as the user's preferences on the gradualness threshold and makes it possible to extract gradual patterns on certain databases where literature approaches fail due to too large search space. Moreover, results from an experimental evaluation on real databases show that the proposed algorithm is scalable, efficient, and can eliminate numerous patterns that do not verify specific gradualness requirements to show a small set of patterns to the user.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
