Data mining algorithms help create valuable functionality for analytic applications. IMSL contains many algorithms for data mining, including feedforward neural networks, genetic algorithms, support vector machines, decision trees, and more. 
 

Genetic Algorithm for Data Mining

A genetic algorithm can provide valuable functionality for many data mining applications. For example, by identifying the best indicators that will determine if a credit card applicant will be a credit risk, or by identifying patterns in purchase behavior to enable companies to better target price discounts.

Text Mining With Naïve Bayes

As most organizations today have more text and documents than humans can keep track of, text mining is becoming an increasingly popular data mining and forecasting tool. With the IMSL C Library Naïve Bayes text mining algorithm, developers can create applications that search websites or customer relationship management system notes for timely and relevant data.

With data forecasting algorithms that range from correlation analysis to Naïve Bayes classification, IMSL data forecasting algorithms help organizations to create fast and accurate data forecasting applications.

Accurate and timely forecasting and predictions can mean a huge competitive edge for companies. IMSL includes advanced forecasting techniques like Auto_ARIMA and Neural Network to help companies create applications with highly effective forecasting functionality.

Auto_ARIMA Advanced Forecasting Routine

The IMSL Library function, Auto_ARIMA, is an advanced forecasting routine for time series analysis with an ARIMA model. With the Auto_ARIMA function, companies can create applications for sales forecasting, commodity pricing (e.g. oil & gas), stock market predictions, semiconductor yield analysis, and more.

Neural Network Forecasting and Classification Functions

Neural network forecasting and classification functions help users discover relationships and valuable information in vast amounts of data. With a high degree of flexibility and control, the IMSL neural network forecasting and classification functions help companies in finance, business analytics, and bioinformatics create fast, effective analytic solutions.

From asset management to investment banking, financial organizations around the world rely on IMSL Numerical Libraries for advanced data analysis. With financial forecasting algorithms like GARCH, ARMA, Auto_ARIMA, and advanced forecasting techniques like Feed Forward Neural Networks, quantitative analysts can create data-driven forecasting for equities, fixed income, currency, and commodities.

Market Volatility Forecasting

With IMSL financial algorithms, quantitative analysts and researchers can use data collection, processing, visualization, and modeling to predict market volatility.

Financial Performance Modeling

Quickly add application functionality for prediction, simulation, optimization, and other financial modeling techniques with IMSL financial modeling algorithms.

Portfolio Optimization

With linear, non-linear, quadratic programming, and other options within the IMSL libraries, asset managers and quantitative analysts can quickly develop versatile portfolio optimization applications.

Financial Risk Management

IMSL financial risk management algorithms can calculate information about range of outcomes, such as best / worst-case, the chances of reaching target goals, and the most likely outcomes.

Trading Strategy Optimization

IMSL financial algorithms like the Genetic algorithm help quantitative analysts create trading strategy optimization applications that identify patterns, opportunities, and limitations in existing strategies.

IMSL includes machine learning algorithms for prediction and pattern recognition suitable for addressing many data science use cases such as credit scoring, target marketing, price/demand modeling, and more. These algorithms include support vector machines, decision trees, stochastic gradient boosting, neural networks, and many other regression, classification, and clustering methods.

IMSL libraries have many proven and advanced AI algorithms that can help teams add artificial intelligence functionality to their applications. IMSL libraries include a wide range of machine learning algorithms and functions that can be applied for unsupervised, supervised, and reinforcement learning – as well as feature learning and anomaly detection.

Genetic Algorithms

Genetic algorithms are increasingly popular for solving optimization, search, and machine learning problems. IMSL implements both the simple genetic algorithm as well as more advanced variations that allow for flexibility for user-provided initial populations, stopping criteria, and phenotype encoding and decoding. 

Cluster Analysis

Cluster analysis can be a useful technique in machine learning and artificial intelligence and can be applied across a variety of fields. IMSL includes functions suitable for multivariate analysis, including hierarchical cluster analysis, k-means cluster analysis, principal component analysis, and factor analysis.

Linear and Logistic Regression

Regression functions are a mainstay in machine learning, and can be used for forecasting, prediction, and in determining the relationships between variables. IMSL libraries feature functions suited to linear regression, including those used for model fitting, statistical inference and diagnostics, as well as polynomial and non-linear regression.

Decision Trees

Practitioners gain valuable insight from trained decision trees that display the decision-making process. Ensemble methods, such as Random Forest and Stochastic Gradient Boosting fit many small trees to reduce noise and maximize predictive accuracy.

IMSL decision tree functions include popular tree fitting algorithms, functions for computing predicted values, printing decision trees, managing decision tree memory, and performing stochastic gradient boosting.

Support Vector Machines

Support vector machines are widely used to detect and classify animals, landscape features, and objects in digital image data. As such they have been useful in many fields, including protein folding research, bioinformatics, and environmental science. IMSL includes functions for training support vector machines, classifying unknown patterns, and freeing allocated memory when it’s no longer needed.

Neural Networks

Neural networks are used to solve a wide variety of problems in data science, includingforecasting, classification, and statistical pattern recognition – all of which are applicable for machine learning and AI applications. IMSL features many neural network functions that make it easy to create and manage a variety of neural network types, including multilayer feedforward neural networks.

Naïve Bayes

A Naïve Bayes classifier can be trained to classify patterns involving thousands of attributes and applied to thousands of patterns. As a result, Naïve Bayes is a preferred algorithm for text mining and other large classification problems. IMSL includes functions for training Naïve Bayes classifiers, classifying patterns using previously trained Naïve Bayes classifiers, as well as storing and retrieving trained classifiers.