Generalized sequential data mining using enhanced object representations based on preliminary clustering profiles

我的研究建议在应用其他数据挖掘和商业智能技术之前，使用非监督聚类来增强数据集，这些技术包括数据摘要、二阶非监督聚类、分类、预测和关联挖掘。受无监督预训练用于深度前馈神经网络收敛的启发，本文的工作将是这一领域向前迈出的重要一步。在第一阶段，我们将使用最先进的集群技术。在后续阶段，我们将探索神经网络以外的数据挖掘技术。 为了说明拟议工作的应用，考虑可用于客户关系管理、库存管理和欺诈检测的金融交易数据集。客户的表示可以包括时间、地理或支出配置文件。在第一阶段，除了总支出、访问频率和最近访问次数等其他原始属性外，这些配置文件还可以用作表示客户的属性。在第二阶段，可以使用包括商业智能、关联挖掘、监督学习(分类和预测)和二阶无监督学习(群集)在内的多种数据挖掘技术来分析扩充的数据集。这种关联挖掘将提供这样的规则：“那些在夏季消费较多的人往往会在周末花费更多”。监督学习将使用金融交易数据中已知的违约、欺诈和其他异常行为的发生率来创建模型，根据客户配置文件预测欺诈和异常的可能性。例如，“假日消费者”倾向于更多地拖欠贷款。随后的无监督学习也将帮助我们了解不同配置文件之间的相关性。例如，“夏季消费者”往往是“非本地消费者”。 我们将使用来自一家批发商、一家零售商的数据和针对金融违约的开放数据集来测试和改进所建议的序列数据挖掘技术，以证明我们建议的建议的有效性。这项申请将不会局限于商业交易。此外，我们还将利用天气和能源消耗模式来建议建筑物的最佳能源控制策略。 虽然这一建议的灵感来自深度学习中的无监督预培训，但它将受益于利用过去50年来更广泛的聚类研究。一种改进的例子是使用集群有效性指数来确定合适的集群数量。此外，第一阶段监督学习还将应用于其他分类和预测技术，如决策树、随机森林和支持向量机。它还可以增强其他数据挖掘技术，如优化、关联挖掘和商业智能。

My research proposes the use of unsupervised clustering to enhance datasets prior to applying other data mining and business intelligence techniques including data summarization, second order unsupervised clustering, classification, prediction, and association mining. Inspired by unsupervised pre-training used in the convergence of deep feed-forward neural networks, the proposed work will be a significant step forward in this area. In the first phase, we will use state-of-the-art clustering techniques. In subsequent phases, we will explore data mining techniques beyond neural networks. To illustrate an application of the proposed work, consider a financial transaction dataset that can be used for customer relationship management, inventory management and fraud detection. A customer's representation can include temporal, geographical, or spending profiles. In phase one, these profiles can be used as attributes for representing customers in addition to other raw attributes such as total spending, frequency and recency of visits. The augmented dataset can be analyzed in the second phase using a diverse set of data mining techniques including business intelligence, association mining, supervised learning (classification and prediction), and second order unsupervised learning (clustering). The association mining will provide rules such as: “Those who spend more in summer tend to spend more on weekends”. The supervised learning will use known incidences of default, fraud and other anomalous behavior in financial transaction data to create models that predict the chances of fraud and anomalies based on customer profiles. For example, "holiday spenders" tend to default more on their loans. The subsequent unsupervised learning will also help us understand correlations between different profiles. For instance, "summer spenders" tend to be "non-local spenders". We will demonstrate the usefulness of our proposal using data from one wholesaler, one retailer, and open datasets for financial defaults to test and refine the proposed sequential data mining techniques. The application will not be restricted to commercial transactions. In addition, we will also use weather and energy consumption patterns to suggest optimal energy control strategies for buildings. While this proposal derives its inspiration from the unsupervised pre-training in deep learning, it will benefit from utilizing broader clustering research from the last fifty years. An example of one improvement is using cluster validity indices to determine the appropriate number of clusters. Furthermore, first-phase supervised learning will also be applied to other classification and prediction techniques such as decision trees, random forests and support vector machines. It can also enhance other data mining techniques such as optimization, association mining and business intelligence.