各类数据挖掘算法缺点
介绍 (Introduction)
In today's world, the amount of data is increasing exponentially whether it is biomedical data, security data or online shopping data, many industries preserve the data in order to analyse it, so that they can serve their customers more effectively through the information which they take out from large preserve data. This taking out or digging out information from huge data sets obtained from different sources and industries is known as Data Mining.
在当今世界，无论是生物医学数据，安全数据还是在线购物数据，数据量都呈指数级增长，许多行业都保留数据以进行分析，以便他们可以通过获取的信息更有效地为客户提供服务从大型保留数据中删除。 从不同来源和行业获得的巨大数据集中提取或挖掘信息的过程被称为数据挖掘。
知识发现 (Knowledge Discovery)
Knowledge discovery is the overall process of extracting knowledge from the huge data sets. It involves the following steps:
知识发现是从海量数据集中提取知识的整个过程。 它涉及以下步骤：
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Data Cleaning – In Data Cleaning the noise and inconsistent data is removed.
数据清理 –在数据清理中，消除了噪音和不一致的数据。
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Data Integration − multiple data sources are combined.
数据集成 -合并了多个数据源。
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Data Selection − only the relevant data is selected from the database.
数据选择 -从数据库中仅选择相关数据。
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Data Transformation − data is consolidated into appropriate forms for mining by performing summary or aggregation operations.
数据转换 -通过执行汇总或聚合操作，将数据合并为适当的形式以进行挖掘。
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Data Mining − this is an intelligent step in which various methods are applied to extract data patterns.
数据挖掘 -这是一个智能步骤，其中采用了各种方法来提取数据模式。
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Pattern Evaluation − data patterns, which can be in different forms like trees, associations, clusters, etc. are evaluated.
模式评估 - 评估数据模式，数据模式可以采用不同的形式，例如树，关联，集群等。
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Knowledge Presentation − this step finally provides knowledge.
知识介绍 -此步骤最终提供知识。
数据挖掘的好处 (Benefits of Data Mining)
The below figure describes the various benefits of data mining.
下图描述了数据挖掘的各种好处。
数据挖掘的缺点 (Disadvantages of Data Mining)
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The concise information obtained by the companies, they can sell it to other companies for money like American Express has sold information about their customers credit card purchases to other company.
公司获得的简明信息，他们可以将其出售给其他公司，例如American Express已将其客户的信用卡购买信息出售给了其他公司。
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Data mining requires advance training and prior knowledge about the tools and softwares to work on.
数据挖掘需要高级培训和有关要使用的工具和软件的先验知识。
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Various data mining tools work in different manners due to different algorithms employed in their design. Therefore, the selection of the correct data mining tools is a very tough task.
由于各种数据挖掘工具在设计中采用了不同的算法，因此它们以不同的方式工作。 因此，选择正确的数据挖掘工具是一项非常艰巨的任务。
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Some times prediction can go wrong and can play havoc for the companies on taking any decision based on that prediction.
有时候，预测可能会出错，并且会对公司根据该预测做出任何决定造成破坏。
数据挖掘的应用 (Applications of Data Mining)
| Applications |
Usage |
| Communications |
Data mining techniques are used in the communication sector to predict customer behavior to offer highly targeted and relevant campaigns. |
| Insurance |
Data mining helps insurance companies to price their products profitable and promote new offers to their new or existing customers. |
| Education |
Data mining benefits educators to access student data, predict achievement levels and find students or groups of students who need extra attention. For example, students who are weak in a science subject. |
| Manufacturing |
By using the help of Data Mining Manufacturers can predict wear and tear of production assets. They can anticipate maintenance which helps them reduce them to minimize downtime. |
| Banking |
Data mining helps the finance sector to get a view of market risks and manage regulatory compliance. It helps banks to identify probable defaulters to decide whether to issue credit cards, loans, etc. |
| Retail |
Data Mining techniques help retail malls and grocery stores identify and arrange most sellable items in the most attentive positions. It helps store owners to come up with the offer which encourages customers to increase their spending. |
| Service Providers |
Service providers like mobile phone and utility industries use Data Mining to predict the reasons when a customer leaves their company. They analyze billing details, customer service interactions, complaints made to the company to assign each customer a probability score and offer incentives. |
| E-Commerce |
E-commerce websites use Data Mining to offer cross-sells and up-sells through their websites. One of the most famous names is Amazon, which uses Data mining techniques to get more customers into their eCommerce store. |
| 应用领域 |
用法 |
| 通讯技术 |
数据挖掘技术用于通信领域，以预测客户行为，以提供高度针对性和相关性的活动。 |
| 保险 |
数据挖掘可帮助保险公司将其产品定价为可盈利的产品，并向其新客户或现有客户推广新产品。 |
| 教育 |
数据挖掘使教育工作者可以访问学生数据，预测成绩水平并找到需要额外关注的学生或学生群体。 例如，一门科学学科薄弱的学生。 |
| 制造业 |
通过使用数据挖掘的帮助，制造商可以预测生产资产的损耗。 他们可以预见维护，这有助于减少维护时间，从而最大程度地减少停机时间。 |
| 银行业 |
数据挖掘可帮助金融部门了解市场风险并管理法规遵从性。 它可以帮助银行确定可能的违约者，以决定是否发行信用卡，贷款等。 |
| 零售 |
数据挖掘技术可帮助零售购物中心和杂货店在最细心的位置识别并安排最畅销的商品。 它可以帮助商店所有者提出要约，以鼓励顾客增加支出。 |
| 服务供应商 |
手机和公用事业等服务提供商使用数据挖掘来预测客户离职的原因。 他们分析帐单详细信息，客户服务互动，向公司投诉以为每个客户分配概率分数并提供激励措施。 |
| 电子商务 |
电子商务网站使用数据挖掘通过其网站提供交叉销售和追加销售。 亚马逊是最著名的名字之一，它使用数据挖掘技术来吸引更多客户进入他们的电子商务商店。 |
翻译自: https://www.includehelp.com/basics/data-mining-introduction-benefits-disadvantages-and-applications.aspx
各类数据挖掘算法缺点
