Programmer‘s Guide to Data Mining

Author: Zen and the Art of Computer Programming

1 Introduction

With the widespread application and popularization of new communication technologies such as the Internet, mobile Internet, and Internet of Things, more and more people are beginning to use them as data sources, generating massive amounts of data. These data include data from various sensors, as well as unstructured data such as user behavior logs, social network data, search engine keyword data, and email addresses. The processing, analysis and mining of these data are crucial to understanding customer needs, optimizing business operations, and improving service quality. For many scientific researchers engaged in computer vision, pattern recognition, natural language processing, recommendation systems and other fields, they often encounter many difficulties when performing data mining tasks. In order to help everyone solve the problems encountered in the data mining process, this book is written based on actual experience, combined with the author's many years of work experience in machine learning and deep learning, and strives to explain the latest machine learning methods and algorithms in easy-to-understand language. At the same time, it focuses on some problems and challenges that may be encountered in practice. This book is divided into six chapters, with the main contents as follows:

Overview: Introduces the basic concepts, classifications, methods, evaluation indicators and application scenarios of data mining.
Data preprocessing: Introduces how to collect, clean and prepare data, such as missing value processing, outlier detection, data set partitioning, feature extraction and other technologies.
Feature Engineering: Introduces how to use feature engineering technology to improve model effects, such as feature selection, dimensionality reduction, regularization, cross-validation and other technologies.
Model Construction: Introduces model construction methods such as decision trees, support vector machines, neural networks, clustering, association rules, and random forests based on tree structures. And some typical cases are given for each model.
Model evaluation: Introduces commonly used model evaluation indicators, such as accuracy, recall, F1-Score, ROC curve and other performance evaluation indicators; as well as model parameter adjustment methods, such as grid search method, Bayesian parameter adjustment, random Search method etc.
Summary and Outlook: Some main research directions and future development directions of data mining are sorted out.

This book is not only suitable for people related to data science, but also for machine learning and deep learning practitioners. I hope that by studying this book, readers can quickly get started with data mining technology and effectively analyze and mine complex business data.

2. Overview of data mining

2.1 Introduction to data mining

Data mining is a process of analyzing, processing and discovering valuable information based on computer technology. It usually requires processing large amounts of data, extracting effective information, summarizing and expressing this information so that people can understand and act. Its purpose is to analyze, identify, classify, predict or summarize the regularities, patterns, correlations, etc. hidden in the data according to a certain pattern on a large amount of data generated by known or unknown transactions.

Data mining can be used in the following areas:

Exploratory data analysis : Conduct exploratory analysis on large amounts of data to find meaningful patterns, discover hidden signals, and analyze connections and differences between data.
Predictive analysis : Use data mining models to predict, classify, cluster, and correlate tasks.
Optimize products and services : Use data mining technology to develop more accurate and competitive products and services.
Customer analysis : By analyzing user behavior, consumption habits, hobbies and other data, we can obtain user preferences and provide better services for the company.
Financial risk management : Use data mining technology to analyze transaction data, identify high-risk stocks, and carry out risk control.
Medical health management : Use data mining technology to analyze medical diagnosis data, identify patient symptoms, carry out targeted treatment, and improve the success rate of patient treatment.

2.2 Definition of data mining

The definition of data mining consists of three elements: data, mining, and knowledge. Among them, "data" refers to data from different sources and different types; "mining" refers to the process of analyzing, summarizing, sorting, and refining data, with the purpose of discovering and revealing the inner meaning, patterns and laws of the data; "knowledge" refers to It refers to a series of operations to interpret, apply, and promote analysis results, that is, to transform the effective information obtained from mining into executable business decisions or solutions.

2.3 Classification of data mining

Data mining can be divided into the following five categories according to task type, data source and analysis purpose:

Text Mining: Using text data for analysis and mining. For example: data collection, search logs, Web document retrieval, spam filtering, etc.
Image Mining: Using image data for analysis and mining. For example: face recognition, image summary, image classification, image search, object recognition, video surveillance, etc.
Sequence Mining: Using time series data for analysis and mining. For example: motion trajectory analysis, traffic flow prediction, stock market analysis, etc.
Structure Mining: Using structured data for analysis and mining. For example: telecommunications call data analysis, aerospace data analysis, logistics order data analysis, etc.
Semi-structured data mining: Use semi-structured data for analysis and mining. For example: Douban film review data analysis, Weibo hot spot analysis, etc.

2.4 Application scenarios of data mining

Data mining can be applied to the following scenarios:

Discover patterns and trends: Perform statistical analysis, machine learning algorithm training, cluster analysis, and correlation analysis on historical data to discover patterns and rules hidden in the data, and conduct data prediction, risk assessment, anomaly detection, etc.
Provide suggestions: Based on the results of mining analysis, provide enterprises with business support, product suggestions, sales forecasts, service quality optimization, innovative product development and other suggestions.
Feedback system: By analyzing user behavior, order data, web logs, social media data, Tieba posts, etc., it can realize functions such as intelligent customer service, personalized recommendations, product sorting, and payment analysis.
Manufacturing production control: Monitor the operation of factory equipment and production lines, and take safeguard measures in advance to avoid failures; model the degree of automation of the manufacturing process, accurately grasp the relationship between product quality and efficiency, and save costs for manufacturing ,Increase productivity.

3. Data preprocessing

3.1 The role of data preprocessing

The purpose of data preprocessing is to make the data have good quality and structure so that subsequent data mining tasks can proceed smoothly. Data preprocessing is divided into three stages: data collection, data cleaning, data transformation and normalization.

Data collection

Data collection refers to obtaining original data from various channels (databases, files, API interfaces, crawlers, etc.).

There are two ways of data collection: regular collection and real-time collection. Regular collection means frequent collection, and real-time collection means meeting real-time requirements. Generally, the older the data, the more expensive it is to acquire, so regularly collected data dominates, but when the data becomes outdated or updates slowly, real-time data gets more attention.

Clean data

Data cleaning refers to operations such as checking, modifying, filtering, merging, and converting original data to ensure data quality and integrity.

The basic operations involved in data cleaning are:

Field standardization: Unify all fields into the same format to facilitate subsequent analysis.
Missing value processing: add missing values to ensure data quality.
Outlier detection: Discover outliers in the data and use some statistical models to determine whether they should be deleted.
Duplicate record processing: For the same record, only one is retained.
Field splitting: If there are multiple values for a field, it can be split into different columns.
Field merging: Merge two similar fields to facilitate subsequent analysis.
Data conversion: Convert certain fields, such as timestamp conversion, encoding conversion, etc.
Data verification: Check whether the data conforms to the rules to prevent data pollution.

Data transformation and normalization

Data conversion and normalization refers to converting data into a standard form or calculating the required parameters for subsequent analysis. Data transformation and normalization can improve data quality and facilitate analysis.

The basic operations involved in data conversion and normalization are:

Standardization: Convert the data into a distribution with a mean of 0 and a variance of 1 to facilitate subsequent machine learning algorithm processing.
Binning: Discretize continuous variables into several discrete bins to facilitate subsequent analysis.
Normalization: Map data to between 0 and 1 to facilitate subsequent comparison.
Encoding conversion: Encoding labels, such as converting different categories into numbers, to facilitate processing by machine learning algorithms.

3.2 Data preprocessing methods

There are four main methods of data preprocessing:

Regularization: Convert data into a standard form through certain rules or algorithms.
Encoding: Encode labels, convert text into numbers, and convert dates into numbers.
Split: Divide the data set into training set, test set, and validation set according to proportion.
Data transformation: Transform the data, such as normalization, standardization, normal distribution transformation, etc.

4. Feature engineering

4.1 Introduction to feature engineering

Feature engineering is the process of extracting meaningful features from raw data to create effective machine learning models. Feature engineering helps improve the model's predictive ability, reduce data noise, improve the model's robustness, and provide better data for the model.

4.2 The role of feature engineering

Feature engineering has three functions:

Provide useful input data for training and prediction of machine learning models.
Increase the nonlinearity of the model and improve the robustness of the model through feature selection, combination, transformation, etc.
Perform normalization, standardization, coding and other operations on the data to make the data easier to process by machine learning algorithms.

4.3 Principles of feature engineering

There are three basic principles of feature engineering:

Be able to represent real-world problems or goals rather than concepts and assumptions in machine learning algorithms.
Practical limitations of the algorithms used in feature engineering need to be taken into account, such as memory, computing resources, model limitations, etc.
You cannot rely on one algorithm or model alone and can only be used before or after other algorithms or models.

4.4 Feature engineering process

The feature engineering process is generally divided into the following steps:

Clarity of requirements: Clearly know what problem is to be solved and how is it to be solved?
Data collection: Obtain data, including existing structured data and future data sources.
Data exploration and preliminary processing: Explore the data to see which feature values can be used for feature engineering.
Feature selection and extraction: Select features that can be used to train the machine learning model and perform feature extraction.
Data conversion and encoding: Convert data into numerical form so that it can be accepted by machine learning models.
Data normalization and standardization: Normalize or standardize the data so that the data is on the same scale.
Feature crossover: Add new features after feature crossover to the data to enhance feature diversity.
Feature dimensionality reduction: Reduce the dimensionality of data to an appropriate dimension, reduce the number of features, and reduce data redundancy.
Feature engineering model evaluation: Model evaluation of features after feature engineering to determine whether the expected results are achieved.
Selection of test set: Divide the data into training set, test set, and validation set for final evaluation of the model.