Pattern Recognition and Machine Learning

Variational Bayesian learning is one of the most popular methods in machine learning. Designed for researchers and graduate students in machine learning, this book summarizes recent developments in the non-asymptotic and asymptotic theory of variational Bayesian learning and suggests how this theory can be applied in practice. The authors begin by developing a basic framework with a focus on conjugacy, which enables the reader to derive tractable algorithms. Next, it summarizes non-asymptotic theory, which, although limited in application to bilinear models, precisely describes the behavior of the variational Bayesian solution and reveals its sparsity inducing mechanism. Finally, the text summarizes asymptotic theory, which reveals phase transition phenomena depending on the prior setting, thus providing suggestions on how to set hyperparameters for particular purposes. Detailed derivations allow readers to follow along without prior knowledge of the mathematical techniques specific to Bayesian learning.

Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics.

Summary

Chapter Objectives

✓ To understand the need of an operational data store in OLTP and OLAP systems

✓ To understand data warehousing, its benefits and architecture

✓ To do a comparative study on OLAP, OLTP and ODS.

✓ To comprehend the concept of data mart.

In order to understand what an Operational Data Store is, it is very important to know what led to such Operational Data Stores and what the limitations were of OLTP systems to answer management queries.

The Need for an Operational Data Store (ODS)

Online Transaction Processing (OLTP) systems have become popular due to their versatility. Right from financial transactions to daily log operations, these are being used by myriad multinational companies and world level organizations to record transaction details.

In the present scenario, organizations and their branches operate in many locations across the world. Each, such, branch generates massive amounts of data. The management of a large retail chain operating from multiple locations, for example, will at the end of day, want to know about transactions done that day. One could take the case of a Dominos Pizza store, where the management needs to know the total sales for that day or other details such as the number and types of pizzas sold. Such companies rely on OLTP systems to get data from multiple stores spanning the world. On these OLTP systems, queries usually run on an indexed database, as this makes the searching fast and efficient. But unfortunately, data spread over multiple systems leads to a plethora of technical errors when carrying out the simple task of running queries on data stored on OLTP systems.

When dealing with corporate data it is also necessary to maintain correct and accurate information, in order to provide swift customer support services. This is possible if the data is acquired from all information sources.

It is very important for any organization's management to know the real state of affairs of their organization. However, OLTP systems fail badly, as they were not designed to support management queries. Management queries are very complex and require multiple joins and aggregations while being written. To overcome this limitation of OLTP systems some solutions were proposed, which are as follows.

Summary

Chapter Objectives

✓ To comprehend the concept of association mining and its applications

✓ To understand the role of support, confidence and lift

✓ To comprehend the Naïve algorithm, Its limitations and improvements

✓ To learn about approaches for transaction database storage

✓ To understand and demonstrate the use of the apriori algorithm and the direct hashing and pruning algorithm

✓ To use dynamic Itemset counting to identify association rules

✓ To use FP growth for mining frequent patterns without candidate generation

Introduction to Association Rule Mining

Association rule mining often known as ‘market basket’ analysis is very effective technique to find the association of sale of item X with item Y. In simple words, market basket analysis consists of examining the items in the baskets of shoppers checking out at a market to see what types of items ‘go together’ as illustrated in Figure 9.1.

It would be useful to know, when people make a trip to the store, what kind of items do they tend to buy during that same shopping trip? For example, as shown in Figure 9.1, a database of customer transactions (i.e., shopping baskets) is given where each transaction consists of a set of items (i.e., products purchased during a visit). Association rule mining is used to identify groups of items which are frequently purchased together (customers’ purchasing behavior). For example, ‘IF one buys bread and milk, THEN he/she also buys eggs with high probability.’ This information is useful for the store manager for better planning of stocking items in the store to improve its sale and efficiency.

Let us suppose that the store manager, receives customer complaints about heavy rush in his store and its consequent slow working. He may then decide to place associated items such as bread and milk together, so that customers can buy the items easier and faster than if these were at a distance. It also improves the sale of each product. In another scenario, let us suppose his store is new and the store manager wishes to display all its range of products to prospective customers.

Summary

Chapter Objectives

✓ To understand the need for online analytical processing

✓ To distinguish OLAP from OLTP and data mining

✓ To comprehend the representation of multi-dimensional view of data

✓ To understand and apply the concept of data cube in real world applications

✓ To implement multi-dimensional view of data in Oracle

✓ To understand different types of OLAP Servers such as ROLAP and MOLAP

✓ To able to perform different OLAP Operations on the database

Introduction to Online Analytical Processing

As there is a huge growth in data warehousing in recent past, the demand for more powerful access tools that help in advanced analytical processing from historical data has increased. Online Analytical Processing (OLAP) and data mining are two types of access tools that have been developed to meet the demands of management users. OLAP and data mining are distinct in what they offer the user and due to this they are complementary technologies.

OLAP is a design paradigm that provides a method to extract useful information from a physical data store. It aggregates information from multiple systems and provides summarized information/view to the management, while data mining is used to find hidden patterns within the data.

OLAP summarizes data and makes forecasts. For example, it answers operational questions like ‘What are the average sales of cycles, by region and by year?’ Data mining discovers hidden patterns in data and operates at a detailed level instead of a summary level. For instance, in a telecom industry where customer churn is a key factor, data mining would answer questions like, ‘Who is likely to shift service providers and what are the reasons for that?’ In conclusion, OLAP answers questions such as ‘Is this true?’ while data mining answers questions as ‘Why is this happening? And what might happen if …?’

Thus, OLAP and data mining complement each other and help the management in better decision taking. An environment that consists of a data warehouse/data marts together including tools such as OLAP and/or data mining are collectively known as Business Intelligence (BI) technologies.

Defining OLAP

Dr E.F. Codd (1993) has defined OLAP as ‘the dynamic synthesis, analysis, and consolidation of large volumes of multi-dimensional data’.

From this definition, it is clear that OLAP deals with multi-dimensional view of data as compared to a simple relational database view.

Summary

Chapter Objectives

✓ To apply the K-means algorithm in Weka and R language

✓ To interpret the results of clustering

✓ To identify the optimum number of clusters

✓ To apply classification on un-labeled data by using clustering as an intermediate step

Introduction

As discussed earlier, if data is not labeled then we can analyze this data by performing a clustering analysis, where clustering refers to the task of grouping a set of objects into classes of similar objects.

In this chapter, we will apply clustering on Fisher's Iris dataset. We will use clustering algorithms to group flower samples into clusters with similar flower dimensions. These clusters then become possible ways to group flowers samples into species. We will implement a simple k-means algorithm to cluster numerical attributes with the help of Weka and R.

In the case of classification, we know the attributes and classes of instances. For example, the flower dimensions and classes were already known to us for the Iris dataset. Our goal was to predict the class of an unknown sample as shown in Figure 8.1.

Earlier, we used the Weka J48 classification algorithm to build a decision tree on Fisher's Iris dataset using samples with known class, which helped in predicting the class of unknown samples. We used the flower's Sepal length and width, and the Petal length and width as the specific attributes for this. Based on flower dimensions and using this tree, we can identify an unknown Iris as one of three species, Setosa, Versicolor, and Virginica.

In clustering, we know the attributes for the instances, but we don't know the classes. For example, we know the flower dimensions for samples of the Iris dataset but we don't know what classes exist as shown in Figure 8.2. Therefore, our goal is to group instances into clusters with similar attributes or dimensions and then identify the class.

In this chapter, we will learn what happens if we don't know what classes the samples belong to, or even how many classes there are, or even what defines a class? Since, Fisher's Iris dataset is already labeled, we will first make this dataset unlabeled by removing the class attribute, i.e., the species column. Then, we will apply clustering algorithms to cluster this data on the basis of its input attributes, i.e., Sepal length, Sepal width, Petal length, and Petal width.

Summary

Chapter Objectives

✓ To comprehend the concept, types and working of classification

✓ To identify the major differences between classification and regression problems

✓ To become familiar about the working of classification

✓ To introduce the decision tree classification system with concepts of information gain and Gini Index

✓ To understand the workings of the Naïve Bayes method

Introduction to Classification

Nowadays databases are used for making intelligent decisions. Two forms of data analysis namely classification and regression are used for predicting future trends by analyzing existing data. Classification models predict discrete value or class, while Regression models predict a continuous value. For example, a classification model can be built to predict whether India will win a cricket match or not, while regression can be used to predict the runs that will be scored by India in a forthcoming cricket match.

Classification is a classical method which is used by machine learning researchers and statisticians for predicting the outcome of unknown samples. It is used for categorization of objects (or things) into given discrete number of classes. Classification problems can be of two types, either binary or multiclass. In binary classification the target attribute can only have two possible values. For example, a tumor is either cancerous or not, a team will either win or lose, a sentiment of a sentence is either positive or negative and so on. In multiclass classification, the target attribute can have more than two values. For example, a tumor can be of type 1, type 2 or type 3 cancer; the sentiment of a sentence can be happy, sad, angry or of love; news stories can be classified as weather, finance, entertainment or sports news.

Some examples of business situations where the classification technique is applied are:

Summary

Chapter Objectives

✓ To understand the need for data preprocessing.

✓ To identify different phases of data preprocessing such as data cleaning, data integration, data transformation and data reduction

Need for Data Preprocessing

For any data analyst, one of the most important concerns is ‘Data’. In fact, the representation and quality of data which is being used for carrying out an analysis is the first and foremost concern to be addressed by any analyst. In the context of data mining and machine learning, ‘Garbage in, Garbage out’, is a popular saying while working with large quantities of data.

Commonly, we end up having lot of noisy data; as an example, income: -400 i.e. negative income. Sometimes, we may have unrealistic and impossible combinations of data, for example, in a record, Gender-Male may be entered as Pregnant-Yes. Obviously absurd! because males do not get pregnant. We also suffer due to missing values and other data anomalies. Analyzing such sets of data, that have not been screened before analysis can cause misleading results. Hence, data preprocessing is the first step for any data mining process.

Data preprocessing is a data mining technique that involves transformation of raw data into an understandable format, because real world data can often be incomplete, inconsistent or even erroneous in nature. Data preprocessing resolves such issues. Data preprocessing ensures that further data mining process are free from errors. It is a prerequisite preparation for data mining, it prepares raw data for the core processes.

The University Management System Example

For any University Management System, a correct set of information about their students or vendors is of utmost importance in order to contact them. Hence, accurate and up to date student information is always maintained by a university. Correspondence sent to wrong address would, for instance, lead to a bad impression about the respective university.

Millions of Customer Support Centres across the globe also maintain correct and consistent data about their customers. Imagine a case where a call centre executive is not able to identify the client or customer that he is handling, through his phone number. Such scenarios suggest how reputation is at stake when it comes to accuracy of data. But, on the other hand obtaining the correct details of students or customers is a very challenging task.

Summary

Chapter Objectives

✓ To discuss the major issues of relational databases

✓ To understand the need for NoSQL

✓ To comprehend the characteristics of NoSQL

✓ To understand different data models of NoSQL

✓ To understand the concept of the CAP theorem

✓ To discuss the future of NoSQL

After about half a century of dominance of relational database, the current excitement about NoSQL databases comes as a big surprise. In this chapter, we'll explore the challenges faced by relational databases due to changing technological paradigms and why the current rise of NoSQL databases is not a flash in the pan.

Let us start our discussion by looking at relational databases.

The Rise of Relational Databases

Dr E. F Codd proposed the relational model in 1969. It was soon adopted by the mainstream software industries due to its simplicity and efficiency replacing hierarchical and network models that were prevalent at that time. The timeline showing the rise of the relational model is depicted in Figure 15.1.

The reasons for the success of relational databases were their simplicity, the power of SQL, support for transaction management, concurrency control, and recovery management.

Major Issues with Relational Databases

The relational data model organizes data in rows and columns that are arranged in a tabular form. In the relational model, a row is known as a tuple which is a set of key-value pairs and a relation is a set of these tuples. All operations in SQL consume and return relations. This foundation based on relations provides a certain elegance and simplicity, but it also suffers some limitations. The values in a relational tuple have to be simple (atomic)—they cannot contain any structure, such as a nested record or a list.

This limitation is not true for in-memory data structures, which can take on much richer structures than relations. As a result, if you want to use a richer in-memory data structure, you would have to translate it to a relational representation to store it on disk. This problem is known as impedance mismatch i.e. two different representations that require inter-translation as shown in Figure 15.2.

The impedance mismatch is a major source of frustration for application developers. In the 1990s many experts believed that impedance mismatch would lead to relational databases being replaced with databases that replicate the in-memory data structures to disk.

Summary

Chapter Objectives

✓ To demonstrate the use of the association mining algorithm.

✓ To apply association mining on numeric data

✓ To comprehend the use of class association rules

✓ To compare the decision tree classifier with association mining

✓ To conduct association mining with R language

Association Mining with Weka

Let us consider the ‘to-play-or-not-to-play’ dataset given in Figure 10.1 for getting hands on experience with association mining in Weka. This dataset is available as default dataset in the data folder of Weka with the file name weather.nominal.arff.

This dataset has four attributes describing weather conditions and a fifth attribute is a class attribute that indicates based on the weather conditions of the day, whether Play was held or not. There are 14 instances, or samples in this dataset.

It is important to note that in classification, we are interested in assigning the output attribute to play or no play. But in Association mining we are interested in finding association rules based on the associations between all the attributes that came together. Thus, in association we do not take class attributes into consideration.

If we compare this dataset with the transactions dataset discussed in the last chapter for market basket analysis, you can find equivalence between transaction id and data items purchased in that transaction.

Here, No. 1 to 14, i.e. the instances act as transaction ids and the values of attributes given in the row corresponding to the given instance are acting as data items for that instance. Here we are interested in finding associations by observing the facts like Outlook = sunny AND Temperature = hot is more common than the association of Outlook = sunny AND Temperature = cooloccurring together as shown in Figure 10.2.

Weka contains an Associate tab which aids in applying different association algorithms in order to find association rules from datasets. One such algorithm is the Predictive Apriori association algorithm that optimally combines support and confidence to calculate a value called predictive accuracy as depicted in Figure 10.3.

The user only needs to specify how many rules they would like the algorithm to generate, and the algorithm takes care of optimizing support and confidence to find the best rules.

Summary

Chapter Objectives

✓ To understand what is meant by web mining and its types

✓ To understand the working of the HITS algorithm

✓ To know the brief history of search engines

✓ To understand a search engine's architecture and its working

✓ To understand the PageRank algorithm and its working

✓ To understand the concepts of precision and recall

Introduction

Since Berners-Lee (inventor of the World Wide Web) created the first web page in 1991, there has been an exponential growth in the number of websites worldwide. As of 2018, there were 1.8 billion websites in the world. This growth has been accompanied with another exponential increase in the amount of data available and the need to organize this data in order to extract useful information from it.

Early attempts to organize such data included creation of web directories to group together similar web pages. The web pages in these directories were often manually reviewed and tagged based on keywords. As time passed by, search engines became available which employed a variety of techniques in order to extract the required information from the web pages. These techniques are called web mining. Formally, web mining is the application of data mining techniques and machine learning to find useful information from the data present in web pages.

Web mining is divided into three parts, i.e. web content mining, structure mining, and usage mining as shown in Figure 11.1.

We will discuss each type of web mining in brief.

Web Content Mining

Web content mining deals with extracting relevant knowledge from the contents of a web page. During content mining, we totally ignore how other web pages link to a given web page or how users interact with it. A trivial approach to web content mining is based on location and frequency of keywords. But this gives rise to two problems: first, the problem of scarcity and second, the problem of abundance. The problem of scarcity occurs with those queries that either generate a few results or no results at all. The problem of abundance occurs with the queries that generate too many search results. The root cause of both the problems is the nature of data present on the web. The data is usually present in the form of HTML which is semi-structured and useful information is generally scattered across multiple web pages.

Pattern Recognition and Machine Learning

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4 - Model-based Classification

3 - Dealing with Difficulties

Contents

Variational Bayesian Learning Theory

Model-Based Clustering and Classification for Data Science

12 - Data Warehouse

Summary

List of Figures

9 - Association Mining

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List of Tables

Dedication

14 - Online Analytical Processing

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8 - Implementing Clustering with Weka and R

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Index

5 - Classification

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4 - Data Preprocessing

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Frontmatter

15 - Big Data and NoSQL

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10 - Implementing Association Mining with Weka and R

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11 - Web Mining and Search Engines

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Pattern Recognition and Machine Learning

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