There are numerous data analysis libraries that help us to process and analyze data. They use different programming languages, and have different advantages and disadvantages of solving various data analysis problems. Now, we will introduce some common libraries that may be useful for you. They should give you an overview of the libraries in the field. However, the rest of this book focuses on Python-based libraries.
Some of the libraries that use the Java language for data analysis are as follows:
Weka: This is the library that I became familiar with the first time I learned about data analysis. It has a graphical user interface that allows you to run experiments on a small dataset. This is great if you want to get a feel for what is possible in the data processing space. However, if you build a complex product, I think it is not the best choice, because of its performance, sketchy API design, non-optimal algorithms, and little documentation (http://www.cs.waikato.ac.nz/ml/weka/).
Mallet: This is another Java library that is used for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine-learning applications on text. There is an add-on package for Mallet, called GRMM, that contains support for inference in general, graphical models, and training of Conditional random fields (CRF) with arbitrary graphical structures. In my experience, the library performance and the algorithms are better than Weka. However, its only focus is on text-processing problems. The reference page is at http://mallet.cs.umass.edu/.
Mahout: This is Apache's machine-learning framework built on top of Hadoop; its goal is to build a scalable machine-learning library. It looks promising, but comes with all the baggage and overheads of Hadoop. The homepage is at http://mahout.apache.org/.
Spark: This is a relatively new Apache project, supposedly up to a hundred times faster than Hadoop. It is also a scalable library that consists of common machine-learning algorithms and utilities. Development can be done in Python as well as in any JVM language. The reference page is at https://spark.apache.org/docs/1.5.0/mllib-guide.html.
Here are a few libraries that are implemented in C++:
Vowpal Wabbit: This library is a fast, out-of-core learning system sponsored by Microsoft Research and, previously, Yahoo! Research. It has been used to learn a tera-feature (1012) dataset on 1,000 nodes in one hour. More information can be found in the publication at http://arxiv.org/abs/1110.4198.
MultiBoost: This package is a multiclass, multi label, and multitask classification boosting software implemented in C++. If you use this software, you should refer to the paper published in 2012 in the JournalMachine Learning Research, MultiBoost: A Multi-purpose Boosting Package, D.Benbouzid, R. Busa-Fekete, N. Casagrande, F.-D. Collin, and B. Kégl.
MLpack: This is also a C++ machine-learning library, developed by the Fundamental Algorithmic and Statistical Tools Laboratory (FASTLab) at Georgia Tech. It focusses on scalability, speed, and ease-of-use, and was presented at the BigLearning workshop of NIPS 2011. Its homepage is at http://www.mlpack.org/about.html.
Caffe: The last C++ library we want to mention is Caffe. This is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and community contributors. You can find more information about it at http://caffe.berkeleyvision.org/.
Other libraries for data processing and analysis are as follows:
Statsmodels: This is a great Python library for statistical modeling and is mainly used for predictive and exploratory analysis.
Modular toolkit for data processing (MDP): This is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures (http://mdp-toolkit.sourceforge.net/index.html).
Orange: This is an open source data visualization and analysis for novices and experts. It is packed with features for data analysis and has add-ons for bioinformatics and text mining. It contains an implementation of self-organizing maps, which sets it apart from the other projects as well (http://orange.biolab.si/).
Mirador: This is a tool for the visual exploration of complex datasets, supporting Mac and Windows. It enables users to discover correlation patterns and derive new hypotheses from data (http://orange.biolab.si/).
RapidMiner: This is another GUI-based tool for data mining, machine learning, and predictive analysis (https://rapidminer.com/).
Theano: This bridges the gap between Python and lower-level languages. Theano gives very significant performance gains, particularly for large matrix operations, and is, therefore, a good choice for deep learning models. However, it is not easy to debug because of the additional compilation layer.
Natural language processing toolkit (NLTK): This is written in Python with very unique and salient features.
Here, I could not list all libraries for data analysis. However, I think the above libraries are enough to take a lot of your time to learn and build data analysis applications. I hope you will enjoy them after reading this book.