Reinforcing pattern mining with uncertainty and connectivity

通常，存储在数据库中的数据被认为是发生的事实和观察结果。这些记录通常也被认为是相互独立的。对于许多应用程序域来说都是如此。例如，医院数据库包含相互独立的患者信息。记录可以说明在给定日期对给予给定治疗的特定诊断的访问。观察到的事实被记录为无关的。数据挖掘和分析任务假定记录之间的这种独立性，并认为记录的事实是确定的。然而，在许多实际应用中，观测之间存在一些关系，因此可以被记录下来。例如，一个人是另一个人的朋友、同事或家人的事实可以被记录为联系。在电信技术中，一个电话号码呼叫另一个电话号码的事实在记录之间建立了一种关系。这些是信息网络。在数据分析中忽略这些关系是错失了更好地了解数据的机会。此外，数据中存在不确定因素，这些不确定因素可能来自传感器等测量仪器或与信息源相关的任何置信度。例如，传感器测量的温度可能不是确定的，而是假设在一个值范围内；在不同应用领域中记录的许多值可能只能以一定程度的置信度来确定。这些是概率数据，因为属性的值可以附加概率级别。不确定性也可以归因于数据中的关系，以形成概率信息网络。遗憾的是，大多数现有的数据挖掘方法都假定数据的独立性和确定性。在有效分析概率数据库或概率信息网络以在这种数据集合中发现有用的新知识或模式方面所做的工作很少。这项提议的目的是致力于设计有效和高效的技术，从概率数据库和概率信息网络中挖掘和学习，并展示这些技术在不确定性与数据收集密切相关的实际领域应用中的使用和相关性。

Generally, data stored in databases are considered facts and observations that took place. These records are also typically considered mutually independent. This is true for many application domains. For instance, a hospital database contains information about patients that are independent from each other. A record could state a visit at a given date for a particular diagnostic for which a given treatment is given. The observed facts are recorded as unrelated. Data mining and analysis tasks assume this independence between records and consider the recorded facts to be certain. However, in many real applications some relationships exist between observations and thus could be recorded. For example, the fact that a person is a friend, colleague or family member of another person can be recorded as a connection. In telecommunication, the fact that a phone number calls another telephone number creates a relationship between records. These are information networks. Ignoring these relationships in data analysis is a missed opportunity to get better insights about the data. Moreover, uncertainties in data exist and can come from the measurement instruments such as sensors or any confidence level attached to the source of information. For instance, the temperature measured by a sensor may not be certain but assumed within a range of values; many values recorded in different application domains may only be ascertained with some level of confidence. These are probabilistic data because the value of an attribute could be affixed with a probability level. Uncertainty can also be ascribed to relationships in data to form probabilistic information networks. Unfortunately, most existing data mining approaches assume independence and certainty of data. Very little work has been done on effectively analyzing probabilistic databases or probabilistic information networks to discover useful new knowledge or patterns in such data collections. The purpose of this proposal is to work on devising effective and efficient techniques to mine and learn from probabilistic databases and probabilistic information networks, and show the use and relevance of these techniques in real domains applications where uncertainty is germane to the data collection.