The partition-based methods use a similarity measure to combine objects into groups. A practitioner usually selects the similarity measure for such kinds of algorithms by themself, using prior knowledge about a problem or heuristics to select the measure properly. Sometimes, several measures need to be tried with the same algorithm to choose the best one. Also, partition-based methods usually require either the number of desired clusters or a threshold that regulates the number of output clusters to be explicitly specified.
- Title Page
- Copyright and Credits
- About Packt
- Contributors
- Preface
- Section 1: Overview of Machine Learning
- Introduction to Machine Learning with C++
- Data Processing
- Technical requirements
- Parsing data formats to C++ data structures
- Reading CSV files with the Fast-CPP-CSV-Parser library
- Preprocessing CSV files
- Reading CSV files with the Shark-ML library
- Reading CSV files with the Shogun library
- Reading CSV files with the Dlib library
- Reading JSON files with the RapidJSON library
- Writing and reading HDF5 files with the HighFive library
- Initializing matrix and tensor objects from C++ data structures
- Manipulating images with the OpenCV and Dlib libraries
- Transforming images into matrix or tensor objects of various libraries
- Normalizing data
- Summary
- Further reading
- Measuring Performance and Selecting Models
- Section 2: Machine Learning Algorithms
- Clustering
- Technical requirements
- Measuring distance in clustering
- Types of clustering algorithms
- Examples of using the Shogun library for dealing with the clustering task samples
- Examples of using the Shark-ML library for dealing with the clustering task samples
- Examples of using the Dlib library for dealing with the clustering task samples
- Plotting data with C++
- Summary
- Further reading
- Anomaly Detection
- Dimensionality Reduction
- Classification
- Recommender Systems
- Technical requirements
- An overview of recommender system algorithms 
- Non-personalized recommendations
- Content-based recommendations
- User-based collaborative filtering
- Item-based collaborative filtering
- Factorization algorithms
- Similarity or preferences correlation
- Data scaling and standardization
- Cold start problem
- Relevance of recommendations
- Assessing system quality 
- Understanding collaborative filtering method details 
- Examples of item-based collaborative filtering with C++
- Summary
- Further reading
- Ensemble Learning
- Section 3: Advanced Examples
- Neural Networks for Image Classification
- Technical requirements
- An overview of neural networks 
- Delving into convolutional networks
- What is deep learning?
- Examples of using C++ libraries to create neural networks
- Understanding image classification using the LeNet architecture
- Summary
- Further reading
- Sentiment Analysis with Recurrent Neural Networks
- Section 4: Production and Deployment Challenges
- Exporting and Importing Models
- Deploying Models on Mobile and Cloud Platforms
- Other Books You May Enjoy
Partition-based clustering algorithms
-
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click here login for view all page.