预测分析与数据挖掘：RapidMiner实践

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"《Vijay Kotu, Bala Deshpande] Predictive Analytics (BookZZ.org)》这本书深入探讨了预测分析和数据挖掘的概念与实践，特别提到了使用RapidMiner工具进行操作。作者Vijay Kotu和Bala Deshpande是该领域的专家，他们通过Elsevier出版社的Morgan Kaufmann imprint分享了他们的知识和经验。" 在当今的数据驱动世界中，预测分析已成为企业决策制定的关键部分。这本书的核心在于解释如何利用大数据和先进的分析技术来预测未来趋势，从而帮助企业做出更明智的战略选择。预测分析涵盖了多种方法，包括统计学、机器学习以及数据挖掘，这些都是理解和预测复杂数据模式的基础。数据挖掘是预测分析中的一个重要环节，它涉及到从大量数据中发现有价值的信息。通过数据挖掘，我们可以识别隐藏的模式、关联和异常，这些发现对于业务优化和市场预测至关重要。书中提到的RapidMiner是一款流行的开源数据挖掘工具，它提供了一种直观的工作流程界面，使得非编程背景的用户也能进行复杂的分析任务。预测模型的构建是预测分析的核心任务。这通常包括数据预处理（清洗、转换、归一化等）、特征选择、模型训练、验证和优化。书中可能详细讨论了这些步骤，并提供了实际案例和RapidMiner操作指南，以帮助读者理解如何应用这些理论到实际问题中。此外，版权信息强调了对知识产权的尊重，指出未经许可不得复制或传播书中的内容。对于想要引用或使用书中材料的读者，出版商提供了获取许可的途径，这通常是通过联系出版商或相关的版权机构，如Copyright Clearance Center和Copyright Licensing Agency。《Predictive Analytics》是一本面向数据分析专业人士和对此感兴趣的学习者的实用指南，它不仅提供了理论知识，还结合实际工具提供了实践经验，有助于提升读者在预测分析和数据挖掘领域的技能。

6 CHAPTER 1: Introduction

they have a wide variety of business applications (e.g., A/B testing in

marketing). In general, data mining is a process where many hypotheses

are generated and tested based on observational data. Since the data

mining algorithms are iterative, we can reﬁne the solution in each step.

Queries: Information retrieval systems, like web search engines, use data

mining techniques like clustering to index vast repositories of data. But

the act of querying and rendering of the result is not considered a data

mining process. Query retrieval from databases and slicing and dicing of

data are not generally considered data mining (Tan et al., 2005).

All of the above techniques are used in the steps of a data mining process and

are used in conjunction with the term “data mining.” It is important for the

practitioner to know what makes up a complete data mining process. We will

discuss the speciﬁc steps of a data mining process in the next chapter.

1.3 THE CASE FOR DATA MINING

In the past few decades, we have seen a massive accumulation of data with

the advancement of information technology, connected networks and

businesses it enables. This trend is also coupled with steep decline in the

cost of data storage and data processing. The applications built on these

advancements like online businesses, social networking, and mobile tech-

nologies unleash a large amount of complex, heterogeneous data that are

waiting to be analyzed. Traditional analysis techniques like dimensional

slicing, hypothesis testing, and descriptive statistics can only get us so far

in information discovery. We need a paradigm to manage massive vol-

ume of data, explore the interrelationships of thousands of variables, and

deploy machine learning algorithms to deduce optimal insights from the

data set. We need a set of frameworks, tools, and techniques to intelligently

assist humans to process all these data and extract valuable information

(Piatetsky-Shapiro et al., 1996). Data Mining is one such paradigm that

can handle large volumes with multiple attributes and deploy complex

algorithms to search for patterns from the data. Let’s explore each key moti-

vation for using data mining techniques.

1.3.1 Volume

The sheer volume of data captured by organizations is exponentially increas-

ing. The rapid decline in storage costs and advancements in capturing every

transaction and event, combined with the business need to extract all possible

leverage using data, creates a strong motivation to store more data than ever. A

study by IDC Corporation in 2012 reported that the volume of recorded digital

data by 2012 reached 2.8 zettabytes, and less than 1% of the data are currently

analyzed (Reinsel, December 2012). As data becomes more granular, the need

71.3 The Case for Data Mining

for using large volume data to extract information increases. A rapid increase in

the volume of data exposes the limitations of current analysis methodologies.

In a few implementations, the time to create generalization models is quite

critical and data volume plays a major part in determining the time to devel-

opment and deployment.

1.3.2 Dimensions

The three characteristics of the Big Data phenomenon are high volume, high

velocity, and high variety. Variety of data relates to multiple types of values

(numerical, categorical), formats of data (audio ﬁles, video ﬁles), and appli-

cation of data (location coordinates, graph data). Every single record or data

point contains multiple attributes or variables to provide context for the

record. For example, every user record of an ecommerce site can contain attri-

butes such as products viewed, products purchased, user demographics, fre-

quency of purchase, click stream, etc. Determining what is the most effective

offer an ecommerce user will respond to can involve computing information

along all these attributes. Each attribute can be thought as a dimension in the

data space. The user record has multiple attributes and can be visualized in

multidimensional space. Addition of each dimension increases the complexity

of analysis techniques.

A simple linear regression model that has one input dimension is relatively

easier to build than multiple linear regression models with multiple dimen-

sions. As the dimensional space of the data increases, we need an adaptable

framework that can work well with multiple data types and multiple attributes.

In the case of text mining, a document or article becomes a data point with

each unique word as a dimension. Text mining yields a data set where the

number of attributes ranges from a few hundred to hundreds of thousands of

attributes.

1.3.3 Complex Questions

As more complex data are available for analysis, the complexity of information

that needs to get extracted from the data is increasing as well. If we need to

ﬁnd the natural clusters in a data set with hundreds of dimensions, traditional

analysis like hypothesis testing techniques cannot be used in a scalable fash-

ion. We need to leverage machine-learning algorithms to automate searching

in the vast search space.

Traditional statistical analysis approaches a data analysis problem by assum-

ing a stochastic model to predict a response variable based on a set of input

variables. Linear regression and logistic regression analysis are classic examples

of this technique where the parameters of the model are estimated from the

data. These hypothesis-driven techniques were highly successful in modeling

8 CHAPTER 1: Introduction

simple relationships between response and input variables. However, there is

a signiﬁcant need to extract nuggets of information from large, complex data

sets, where the use of traditional statistical data analysis techniques is limited

(Breiman, 2001)

Machine learning approach the problem of modeling by trying to ﬁnd an

algorithmic model that can better predict the output from input variables.

The algorithms are usually recursive and in each cycle estimate the output and

“learn” from the predictive errors of previous steps. This route of modeling

greatly assists in exploratory analysis since the approach here is not validating

a hypothesis but generating a multitude of hypotheses for a given problem. In

the context of the data problems we face today, we need to deploy both tech-

niques. John Tuckey, in his article “We need both exploratory and conﬁrma-

tory,” stresses the importance of both exploratory and conﬁrmatory analysis

techniques (Tuckey, 1980). In this book, we discuss a range of data mining

techniques, from traditional statistical modeling techniques like regressions

to machine-learning algorithms.

1.4 TYPES OF DATA MINING

Data mining problems can be broadly categorized into supervised or unsuper-

vised learning models. Supervised or directed data mining tries to infer a func-

tion or relationship based on labeled training data and uses this function to

map new unlabeled data. Supervised techniques predict the value of the out-

put variables based on a set of input variables. To do this, a model is developed

from a training data set where the values of input and output are previously

known. The model generalizes the relationship between the input and out-

put variables and uses it to predict for the data set where only input variables

are known. The output variable that is being predicted is also called a class

label or target variable. Supervised data mining needs a sufﬁcient number of

labeled records to learn the model from the data. Unsupervised or undirected

data mining uncovers hidden patterns in unlabeled data. In unsupervised data

mining, there are no output variables to predict. The objective of this class of

data mining techniques is to ﬁnd patterns in data based on the relationship

between data points themselves. An application can employ both supervised

and unsupervised learners.

Data mining problems can also be grouped into classiﬁcation, regression,

association analysis, anomaly detection, time series, and text mining tasks

(Figure 1.2). This book is organized around these data mining tasks. We pres-

ent an overview of the types of data mining in this chapter and will provide

an in-depth discussion of concepts and step-by-step implementations of many

important techniques in the following chapters.

10 CHAPTER 1: Introduction

data mining on text data, the text ﬁles are converted into document vectors

where each unique word is considered an attribute. Once the text ﬁle is con-

verted to document vectors, standard data mining tasks such as classiﬁcation,

clustering, etc. can be applied on text ﬁles. The Feature selection is a process in

which attributes in a data set is reduced to a few attributes that really matter.

A complete data mining application can contain elements of both supervised

and unsupervised techniques. Unsupervised techniques provide an increased

understanding of the data set and hence are sometimes called descriptive data

mining. As an example of how both unsupervised and supervised data mining

can be combined in an application, consider the following scenario. In mar-

keting analytics, clustering can be used to ﬁnd the natural clusters in customer

records. Each customer is assigned a cluster label at the end of the clustering

process. A labeled customer data set can now be used to develop a model that

assigns a cluster label for any new customer record with a supervised classiﬁ-

cation technique.

1.5 DATA MINING ALGORITHMS

An algorithm is a logical step-by-step procedure for solving a problem. In data

mining, it is the blueprint for how a particular data problem is solved. Many of the

algorithms are recursive, where a set of steps are repeated many times until a limit-

ing condition is met. Some algorithms also contain a random variable as an input,

and are aptly called randomized algorithms. A data mining classiﬁcation task can

be solved using many different approaches or algorithms such as decision trees,

artiﬁcial neural networks, k-nearest neighbors (k-NN), and even some regression

algorithms. The choice of which algorithm to use depends on the type of data set,

objective of the data mining, structure of the data, presence of outliers, available

computational power, number of records, number of attributes, and so on. It is up

to the data mining practitioner to make a decision about what algorithm(s) to use

by evaluating the performance of multiple algorithms. There have been hundreds

of algorithms developed in the last few decades to solve data mining problems. In

the next few chapters, we will discuss the inner workings of the most important

and diverse data mining algorithms and their implementations.

Data mining algorithms can be implemented by custom-developed computer

programs in almost any computer language. This obviously is a time-consuming

task. In order for us to focus our time on data and algorithms, we can

leverage data mining tools or statistical programing tools, like R, Rapid-

Miner, SAS Enterprise Miner, IBM SPSS, etc., which can implement these

algorithms with ease. These data mining tools offer a library of algorithms

as functions, which can be interfaced through programming code or conﬁg-

uration through graphical user interfaces. Table 1.1 provides a summary of

data mining tasks with commonly used algorithmic techniques and exam-

ple use cases.

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预测分析与数据挖掘：RapidMiner实践

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