5. Affinity Analysis
by Krishna Choppella, Dr. Uday Kamath, Boštjan Kaluža, Jennifer L. Reese, Richard M
Machine Learning: End-to-End guide for Java developers
- Machine Learning: End-to-End guide for Java developers
- Table of Contents
- Machine Learning: End-to-End guide for Java developers
- Credits
- Preface
- 1. Module 1
- 1. Getting Started with Data Science
- Problems solved using data science
- Understanding the data science problem - solving approach
- Acquiring data for an application
- The importance and process of cleaning data
- Visualizing data to enhance understanding
- The use of statistical methods in data science
- Machine learning applied to data science
- Using neural networks in data science
- Deep learning approaches
- Performing text analysis
- Visual and audio analysis
- Improving application performance using parallel techniques
- Assembling the pieces
- Summary
- 2. Data Acquisition
- 3. Data Cleaning
- 4. Data Visualization
- 5. Statistical Data Analysis Techniques
- 6. Machine Learning
- 7. Neural Networks
- 8. Deep Learning
- 9. Text Analysis
- 10. Visual and Audio Analysis
- 11. Mathematical and Parallel Techniques for Data Analysis
- 12. Bringing It All Together
- 1. Getting Started with Data Science
- 2. Module 2
- 1. Applied Machine Learning Quick Start
- 2. Java Libraries and Platforms for Machine Learning
- 3. Basic Algorithms – Classification, Regression, and Clustering
- 4. Customer Relationship Prediction with Ensembles
- 5. Affinity Analysis
- 6. Recommendation Engine with Apache Mahout
- 7. Fraud and Anomaly Detection
- 8. Image Recognition with Deeplearning4j
- 9. Activity Recognition with Mobile Phone Sensors
- 10. Text Mining with Mallet – Topic Modeling and Spam Detection
- 11. What is Next?
- A. References
- 3. Module 3
- 1. Machine Learning Review
- Machine learning – history and definition
- What is not machine learning?
- Machine learning – concepts and terminology
- Machine learning – types and subtypes
- Datasets used in machine learning
- Machine learning applications
- Practical issues in machine learning
- Machine learning – roles and process
- Machine learning – tools and datasets
- Summary
- 2. Practical Approach to Real-World Supervised Learning
- Formal description and notation
- Data transformation and preprocessing
- Feature relevance analysis and dimensionality reduction
- Model building
- Model assessment, evaluation, and comparisons
- Case Study – Horse Colic Classification
- Summary
- References
- 3. Unsupervised Machine Learning Techniques
- Issues in common with supervised learning
- Issues specific to unsupervised learning
- Feature analysis and dimensionality reduction
- Clustering
- Outlier or anomaly detection
- Real-world case study
- Summary
- References
- 4. Semi-Supervised and Active Learning
- Semi-supervised learning
- Active learning
- Case study in active learning
- Summary
- References
- 5. Real-Time Stream Machine Learning
- Assumptions and mathematical notations
- Basic stream processing and computational techniques
- Concept drift and drift detection
- Incremental supervised learning
- Incremental unsupervised learning using clustering
- Modeling techniques
- Partition based
- Hierarchical based and micro clustering
- Density based
- Grid based
- Validation and evaluation techniques
- Key issues in stream cluster evaluation
- Evaluation measures
- Cluster Mapping Measures (CMM)
- V-Measure
- Other external measures
- Unsupervised learning using outlier detection
- Case study in stream learning
- Summary
- References
- 6. Probabilistic Graph Modeling
- Probability revisited
- Graph concepts
- Bayesian networks
- Representation
- Inference
- Learning
- Learning parameters
- Learning structures
- Measures to evaluate structures
- Methods for learning structures
- Constraint-based techniques
- Search and score-based techniques
- 7. Deep Learning
- Multi-layer feed-forward neural network
- Inputs, neurons, activation function, and mathematical notation
- Multi-layered neural network
- Structure and mathematical notations
- Activation functions in NN
- Training neural network
- Empirical risk minimization
- Parameter initialization
- Loss function
- Gradients
- Feed forward and backpropagation
- How does it work?
- Regularization
- L2 regularization
- L1 regularization
- Limitations of neural networks
- Deep learning
- Building blocks for deep learning
- Rectified linear activation function
- Restricted Boltzmann Machines
- Autoencoders
- Unsupervised pre-training and supervised fine-tuning
- Deep feed-forward NN
- Deep Autoencoders
- Deep Belief Networks
- Deep learning with dropouts
- Sparse coding
- Convolutional Neural Network
- CNN Layers
- Recurrent Neural Networks
- Building blocks for deep learning
- Case study
- Summary
- References
- 8. Text Mining and Natural Language Processing
- NLP, subfields, and tasks
- Text categorization
- Part-of-speech tagging (POS tagging)
- Text clustering
- Information extraction and named entity recognition
- Sentiment analysis and opinion mining
- Coreference resolution
- Word sense disambiguation
- Machine translation
- Semantic reasoning and inferencing
- Text summarization
- Automating question and answers
- Issues with mining unstructured data
- Text processing components and transformations
- Topics in text mining
- Tools and usage
- Summary
- References
- NLP, subfields, and tasks
- 9. Big Data Machine Learning – The Final Frontier
- What are the characteristics of Big Data?
- Big Data Machine Learning
- Batch Big Data Machine Learning
- Case study
- Business problem
- Machine Learning mapping
- Data collection
- Data sampling and transformation
- Spark MLlib as Big Data Machine Learning platform
- A. Linear Algebra
- B. Probability
- D. Bibliography
- Index
- Empirical risk minimization
- Multi-layer feed-forward neural network
- Modeling techniques
- 1. Machine Learning Review
Affinity analysis is the heart of Market basket analysis (MBA). It can discover co-occurrence relationships among activities performed by specific users or groups. In retail, affinity analysis can help you understand the purchasing behavior of customers. These insights can drive revenue through smart cross-selling and upselling strategies and can assist you in developing loyalty programs, sales promotions, and discount plans.
In this chapter, we will look into the following topics:
- Market basket analysis
- Association rule learning
- Other applications in various domains
First, we will revise the core association rule learning concepts and algorithms, such as support, lift, Apriori algorithm, and FP-growth algorithm. Next, we will use Weka to perform our first affinity analysis on supermarket dataset and study how to interpret the resulting rules. We will conclude the chapter by analyzing how association rule learning can be applied in other domains, such as IT Operations Analytics, medicine, and others.
Since the introduction of electronic point of sale, retailers have been collecting an incredible amount of data. To leverage this data in order to produce business value, they first developed a way to consolidate and aggregate the data to understand the basics of the business. What are they selling? How many units are moving? What is the sales amount?
Recently, the focus shifted to the lowest level of granularity—the market basket transaction. At this level of detail, the retailers have direct visibility into the market basket of each customer who shopped at their store, understanding not only the quantity of the purchased items in that particular basket, but also how these items were bought in conjunction with each other. This can be used to drive decisions about how to differentiate store assortment and merchandise, as well as effectively combine offers of multiple products, within and across categories, to drive higher sales and profits. These decisions can be implemented across an entire retail chain, by channel, at the local store level, and even for a specific customer with so-called personalized marketing, where a unique product offering is made for each customer.
MBA covers a wide variety of analysis:
- Item affinity: This defines the likelihood of two (or more) items being purchased together
- Identification of driver items: This enables the identification of the items that drive people to the store and always need to be in stock
- Trip classification: This analyzes the content of the basket and classifies the shopping trip into a category: weekly grocery trip, special occasion, and so on
- Store-to-store comparison: Understanding the number of baskets allows any metric to be divided by the total number of baskets, effectively creating a convenient and easy way to compare the stores with different characteristics (units sold per customer, revenue per transaction, number of items per basket, and so on)
- Revenue optimization: This helps in determining the magic price points for this store, increasing the size and value of the market basket
- Marketing: This helps in identifying more profitable advertising and promotions, targeting offers more precisely in order to improve ROI, generating better loyalty card promotions with longitudinal analysis, and attracting more traffic to the store
- Operations optimization: This helps in matching the inventory to the requirement by customizing the store and assortment to trade area demographics, optimizing store layout
Predictive models help retailers to direct the right offer to the right customer segments/profiles, as well as gain understanding of what is valid for which customer, predict the probability score of customers responding to this offer, and understand the customer value gain from the offer acceptance.
Affinity analysis is used to determine the likelihood that a set of items will be bought together. In retail, there are natural product affinities, for example, it is very typical for people who buy hamburger patties to buy hamburger rolls, along with ketchup, mustard, tomatoes, and other items that make up the burger experience.
While there are some product affinities that might seem trivial, there are some affinities that are not very obvious. A classic example is toothpaste and tuna. It seems that people who eat tuna are more prone to brush their teeth right after finishing their meal. So, why it is important for retailers to get a good grasp of the product affinities? This information is critical to appropriately plan promotions as reducing the price for some items may cause a spike on related high-affinity items without the need to further promote these related items.
In the following section, we'll look into the algorithms for association rule learning: Apriori and FP-growth.
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