Recursive and iterative clustering in granular hierarchical, network, and temporal datasets

聚类是对相似对象进行分组的一种常用的无监督数据挖掘技术。拟议的研究计划将研究一种在粒度环境中进行集群的新的迭代方法。信息组代表一个对象。例如，具有特定购买模式的客户可以由信息粒表示。一个颗粒通常与其他颗粒相连。例如，在分层环境中，客户粒将连接到多个产品粒，反之亦然。在细粒度网络中，电话用户与其他电话用户相连。在细粒度的时间环境中，事件的每日模式与历史和未来的每日模式相关联。传统上，颗粒的聚集是孤立地进行的，没有关于连接的颗粒的聚集的任何信息。提出的研究的主要主题是同时迭代地对所有颗粒进行聚类。每次迭代将使用先前对连接的颗粒进行聚类的结果，直到实现对所有颗粒的稳定聚类。在客户和产品等分层环境中，这意味着客户的集群使用产品集群的配置文件，反之亦然。对于联网的粒度，使用其他连接用户的集群配置文件对电话用户进行集群。在时间粒度聚类中，日常模式将基于历史和未来模式的聚类配置文件进行聚类。这些重复的集群应用在层次中被称为迭代，在网络中被称为递归。集成层次、网络和时态数据的元聚类是一个多方面的项目。由于聚类是无监督的，我们不知道预期的结果，所以研究结果聚类的质量是很重要的。除了得出量化评估外，偏好的概念将被用来根据集群与更令人满意的目标的联系程度来对集群进行估值。迭代和递归算法将进一步改进，以适应模糊和粗糙聚类，允许一个对象属于多个聚类。我们计划为零售、移动电话、工程和金融数据集设计、开发、实现和测试各种不同的集群算法。

Clustering is one of the frequently used unsupervised data mining techniques for grouping similar objects. The proposed research program will investigate a novel iterative approach to clustering in a granular environment. An information granule represents an object. For example, a customer with certain purchasing patterns could be represented by an information granule. A granule is usually connected to other granules. For example, in a hierarchical environment, a customer granule will be connected to a number of product granules and vice versa. In a granular network, phone users are connected to other phone users. In a granular temporal environment, a daily pattern of events is connected to historical and future daily patterns. Traditionally, clustering of granules is done in isolation without any information on clustering of the connected granules. The primary theme of the proposed research is to simultaneously cluster all the granules iteratively. Each iteration will use results of previous clustering of connected granules, until a stable clustering of all the granules is achieved. In a hierarchical environment such as customers and products, it will mean that clustering of customers uses profiles of product clusters, and vice versa. For networked granules, a phone user is clustered using cluster profiles of the other connected users. In a temporal granular clustering, daily patterns will be clustered based on clustered profiles of historical and future patterns. These repeated applications of clustering are termed iterative in a hierarchy and are termed recursive in networks. The integrated meta-clustering of hierarchical, network, and temporal data is a multi-faceted project. Since clustering is unsupervised and we do not know the expected outcomes, it is important to study the quality of the resultant clustering. In addition to deriving quantitative evaluations, the notion of preference will be used to value a cluster based on how well-connected it is to more desirable objects. The iterative and recursive algorithms will be further modified for fuzzy and rough clustering, which allow an object to belong to multiple clusters. We plan to design, develop, implement, and test variations of the clustering algorithms for retail, mobile phone, engineering, and financial datasets.