数据挖掘是公司用来将原始数据转化为有用信息的过程。通过使用软件在大量数据中寻找模式，企业可以更多地了解他们的客户，从而制定更有效的营销策略、增加销售额并降低成本。数据挖掘依赖于有效的数据收集、 仓储和计算机处理。
数据挖掘过程
为了最有效，数据分析师通常在数据挖掘过程中遵循一定的任务流程。如果没有这种结构，分析师可能会在分析过程中遇到问题，如果他们早点做好准备，这些问题很容易避免。数据挖掘过程通常分为以下几个步骤。
第 1 步：了解业务
在接触、提取、清理或分析任何数据之前，了解底层实体和手头项目非常重要。公司试图通过挖掘数据来实现哪些目标？他们目前的业务情况如何？SWOT分析的结果是什么？在查看任何数据之前，挖掘过程首先要了解在过程结束时定义成功的因素。
第 2 步：了解数据
一旦明确定义了业务问题，就该开始考虑数据了。这包括可用的资源、如何安全存储、如何收集信息以及最终结果或分析可能是什么样子。此步骤还批判性地考虑了它们对数据、存储、安全和收集的限制，并评估这些限制将如何影响数据挖掘过程。
第 3 步：准备数据
现在是我们掌握信息的时候了。收集、上传、提取或计算数据。然后对其进行清理、标准化、清除异常值、评估错误并检查其合理性。在数据挖掘的这个阶段，也可能会检查数据的大小，因为信息的过度收集可能会不必要地减慢计算和分析的速度。
第 4 步：构建模型
有了我们干净的数据集，是时候处理这些数字了。数据科学家使用上述数据挖掘类型来搜索关系、趋势、关联或顺序模式。数据也可以输入预测模型，以评估以前的信息如何转化为未来的结果。
第 5 步：评估结果
数据挖掘的以数据为中心的方面通过评估数据模型的结果来结束。分析的结果可以被汇总、解释并呈现给在很大程度上被排除在数据挖掘过程之外的决策者。在此步骤中，组织可以选择根据调查结果做出决策。
第 6 步：实施变更和监控
数据挖掘过程以管理层针对分析结果采取措施而告终。公司可能会认为信息不够有力，或者调查结果与改变方向无关。或者，公司可以根据调查结果进行战略调整。无论哪种情况，管理层都会审查业务的最终影响，并通过识别新的业务问题或机会来重新创建未来的数据挖掘循环。
COMP3425 Data Mining 代写实例
Task
A company that has large sums of money flushing through its hands is under pressure from regulators, knows that stock exchanges run real-time fraud detection schemes, and accepts at face value the upbeat claims made by the proponents of big data analytics. It combines fraud-detection heuristics with inferences drawn from its large transaction database, and generates suspects. It assigns its own limited internal investigation resources to these suspect cases, and refers some of them to law enforcement agencies.
The large majority of the cases investigated internally are found to be spurious. Little is heard back from law enforcement agencies. Some of the suspects discover that they are being investigated, and threaten to take their business elsewhere and to initiate defamation actions. The investigators return to their tried-and-true methods of locating and prioritising suspicious cases. You must answer the following questions, clearly indicating which question you are answering within your submission. The page lengths suggested for each question here are for guidance only; the given page length limit for the overall assignment is mandatory.
Question 1. (1 page) Consider the ACS code of conduct. For each of the six values, taking account of any relevant sub-parts, discuss whether the value was demonstrated in the scenario and to what extent. If you assess any value as largely irrelevant to the scenario, then a very brief reason for this assessment is sufficient.
Question 2. (1/2 page) Consider the 7 US ACM Principles. Looking closely at Principle 1, Awareness, discuss how this principle is applied (or not) in the scenario and identify any “potential harm” that might have ensued.
Question 3. (2 pages) Consider the numbered guidelines in Table 2 of Clarke’s Guidelines for the responsible application of data analytics. From each segment (1 General, 2 Data Acquisition, 3 Data analysis, and 4 Use of the Inferences) choose one guideline that you consider most relevant and important to the scenario and explain its role in the scenario. Justify why it is more relevant than every one of the others in the same segment. Be careful to consider the intention of the guidelines rather than an overly literal interpretation; you may rephrase the chosen guideline for the scenario context where beneficial. For further
explanation of this point, see Section 3 in Clarke’s paper.
Question 4. (1 page) (a) Choose one, numbered guideline (e.g. guideline 3.3) in Table 2 of the Guidelines that you consider to have been disregarded in the scenario. You may choose any guideline that you did not choose for Question 3. Discuss how the failure to consider the guideline could have contributed to the negative outcome of the scenario.
(b) In addition, identify any other potential consequences that could have occurred due to the failure to consider that same guideline. For this purpose, the consequences you identify are not necessarily explicit within the scenario description. You might find it helpful to think of this activity as contributing to a risk assessment process prior to your hypothetical involvement in the analysis work of the scenario.
Question 5. (1 page) Consider the paper by Du et al, Techniques for Interpretable Machine Learning. iscuss whether and how intrinsic and post-hoc interpretability techniques could be applied to the scenario and what benefits could ensue.
