各類數(shù)據(jù)挖掘算法缺點(diǎn)

介紹 (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.

在當(dāng)今世界，無(wú)論是生物醫(yī)學(xué)數(shù)據(jù)，安全數(shù)據(jù)還是在線購(gòu)物數(shù)據(jù)，數(shù)據(jù)量都呈指數(shù)級(jí)增長(zhǎng)，許多行業(yè)都保留數(shù)據(jù)以進(jìn)行分析，以便他們可以通過(guò)獲取的信息更有效地為客戶提供服務(wù)從大型保留數(shù)據(jù)中刪除。 從不同來(lái)源和行業(yè)獲得的巨大數(shù)據(jù)集中提取或挖掘信息的過(guò)程被稱為數(shù)據(jù)挖掘。

知識(shí)發(fā)現(xiàn) (Knowledge Discovery)

Knowledge discovery is the overall process of extracting knowledge from the huge data sets. It involves the following steps:

知識(shí)發(fā)現(xiàn)是從海量數(shù)據(jù)集中提取知識(shí)的整個(gè)過(guò)程。 它涉及以下步驟：

• Data Cleaning – In Data Cleaning the noise and inconsistent data is removed.

  數(shù)據(jù)清理 –在數(shù)據(jù)清理中，消除了噪音和不一致的數(shù)據(jù)。

• Data Integration ? multiple data sources are combined.

  數(shù)據(jù)集成 -合并了多個(gè)數(shù)據(jù)源。

• Data Selection ? only the relevant data is selected from the database.

  數(shù)據(jù)選擇 -從數(shù)據(jù)庫(kù)中僅選擇相關(guān)數(shù)據(jù)。

• Data Transformation ? data is consolidated into appropriate forms for mining by performing summary or aggregation operations.

  數(shù)據(jù)轉(zhuǎn)換 -通過(guò)執(zhí)行匯總或聚合操作，將數(shù)據(jù)合并為適當(dāng)?shù)男问揭赃M(jìn)行挖掘。

• Data Mining ? this is an intelligent step in which various methods are applied to extract data patterns.

  數(shù)據(jù)挖掘 -這是一個(gè)智能步驟，其中采用了各種方法來(lái)提取數(shù)據(jù)模式。

• Pattern Evaluation ? data patterns, which can be in different forms like trees, associations, clusters, etc. are evaluated.

  模式評(píng)估 - 評(píng)估數(shù)據(jù)模式，數(shù)據(jù)模式可以采用不同的形式，例如樹(shù)，關(guān)聯(lián)，集群等。

• Knowledge Presentation ? this step finally provides knowledge.

  知識(shí)介紹 -此步驟最終提供知識(shí)。

數(shù)據(jù)挖掘的好處 (Benefits of Data Mining)

The below figure describes the various benefits of data mining.

下圖描述了數(shù)據(jù)挖掘的各種好處。

數(shù)據(jù)挖掘的缺點(diǎn) (Disadvantages of Data Mining)

• 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.

  公司獲得的簡(jiǎn)明信息，他們可以將其出售給其他公司，例如American Express已將其客戶的信用卡購(gòu)買信息出售給了其他公司。

• Data mining requires advance training and prior knowledge about the tools and softwares to work on.

  數(shù)據(jù)挖掘需要高級(jí)培訓(xùn)和有關(guān)要使用的工具和軟件的先驗(yàn)知識(shí)。

• 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.

  由于各種數(shù)據(jù)挖掘工具在設(shè)計(jì)中采用了不同的算法，因此它們以不同的方式工作。 因此，選擇正確的數(shù)據(jù)挖掘工具是一項(xiàng)非常艱巨的任務(wù)。

• Some times prediction can go wrong and can play havoc for the companies on taking any decision based on that prediction.

  有時(shí)候，預(yù)測(cè)可能會(huì)出錯(cuò)，并且會(huì)對(duì)公司根據(jù)該預(yù)測(cè)做出任何決定造成破壞。

數(shù)據(jù)挖掘的應(yīng)用 (Applications of Data Mining)

ApplicationsUsage

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.

應(yīng)用領(lǐng)域 用法

通訊技術(shù)	數(shù)據(jù)挖掘技術(shù)用于通信領(lǐng)域，以預(yù)測(cè)客戶行為，以提供高度針對(duì)性和相關(guān)性的活動(dòng)。
保險(xiǎn)	數(shù)據(jù)挖掘可幫助保險(xiǎn)公司將其產(chǎn)品定價(jià)為可盈利的產(chǎn)品，并向其新客戶或現(xiàn)有客戶推廣新產(chǎn)品。
教育	數(shù)據(jù)挖掘使教育工作者可以訪問(wèn)學(xué)生數(shù)據(jù)，預(yù)測(cè)成績(jī)水平并找到需要額外關(guān)注的學(xué)生或?qū)W生群體。 例如，一門科學(xué)學(xué)科薄弱的學(xué)生。
制造業(yè)	通過(guò)使用數(shù)據(jù)挖掘的幫助，制造商可以預(yù)測(cè)生產(chǎn)資產(chǎn)的損耗。 他們可以預(yù)見(jiàn)維護(hù)，這有助于減少維護(hù)時(shí)間，從而最大程度地減少停機(jī)時(shí)間。
銀行業(yè)	數(shù)據(jù)挖掘可幫助金融部門了解市場(chǎng)風(fēng)險(xiǎn)并管理法規(guī)遵從性。 它可以幫助銀行確定可能的違約者，以決定是否發(fā)行信用卡，貸款等。
零售	數(shù)據(jù)挖掘技術(shù)可幫助零售購(gòu)物中心和雜貨店在最細(xì)心的位置識(shí)別并安排最暢銷的商品。 它可以幫助商店所有者提出要約，以鼓勵(lì)顧客增加支出。
服務(wù)供應(yīng)商	手機(jī)和公用事業(yè)等服務(wù)提供商使用數(shù)據(jù)挖掘來(lái)預(yù)測(cè)客戶離職的原因。 他們分析帳單詳細(xì)信息，客戶服務(wù)互動(dòng)，向公司投訴以為每個(gè)客戶分配概率分?jǐn)?shù)并提供激勵(lì)措施。
電子商務(wù)	電子商務(wù)網(wǎng)站使用數(shù)據(jù)挖掘通過(guò)其網(wǎng)站提供交叉銷售和追加銷售。 亞馬遜是最著名的名字之一，它使用數(shù)據(jù)挖掘技術(shù)來(lái)吸引更多客戶進(jìn)入他們的電子商務(wù)商店。

翻譯自: https://www.includehelp.com/basics/data-mining-introduction-benefits-disadvantages-and-applications.aspx

各類數(shù)據(jù)挖掘算法缺點(diǎn)

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