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

我的研究建议使用无监督聚类来增强数据集，然后再应用其他数据挖掘和商业智能技术，包括数据摘要，二阶无监督聚类，分类，预测和关联挖掘。受深度前馈神经网络收敛中使用的无监督预训练的启发，拟议的工作将是该领域的重要一步。 在第一阶段，我们将使用最先进的聚类技术。在后续阶段，我们将探索神经网络之外的数据挖掘技术。* 为了说明所提出的工作的应用，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、地理或消费概况。在第一阶段，除了其他原始属性（如总支出、访问频率和最近访问）之外，这些配置文件还可以用作表示客户的属性。在第二阶段，可以使用各种数据挖掘技术来分析增强的数据集，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（聚类）。关联挖掘将提供规则，例如：“那些在夏天花费更多的人倾向于在周末花费更多”。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发生率来创建模型，根据客户资料预测欺诈和异常的可能性。例如，“假日消费者”往往拖欠更多的贷款。随后的无监督学习也将帮助我们理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”。我们将使用来自一个批发商，一个零售商和开放数据集的金融违约测试和完善拟议的顺序数据挖掘技术的数据来证明我们的建议的有用性。申请将不限于商业交易。此外，我们还将利用天气和能源消耗模式为建筑物提出最佳能源控制策略。虽然这个建议的灵感来自于深度学习中的无监督预训练，但它将受益于利用过去50年来更广泛的聚类研究。一个改进的例子是使用聚类有效性指数来确定聚类的适当数量。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.