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Top 10 Anomaly Detection Software

Top 10 Anomaly Detection Software

Top 10 Anomaly Detection Software
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Anomaly Detection Software is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal.Supervised anomaly detection techniques require a data set that has been labeled as normal and abnormal and involves training a classifier.Semi supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set, and then testing the likelihood of a test instance to be generated by the learnt model.

Some of the popular anomaly detection techniques are Density-based techniques (k-nearest neighbor,local outlier factor,Subspace and correlation-based, outlier detection, One class support vector machines, Replicator neural networks, Cluster analysis-based outlier detection, Deviations from association rules and frequent itemsets, Fuzzy logic based outlier detection and Ensemble techniques.

Prelert, Anodot, Loom Systems, Interana, Numenta are some of the Top Anomaly Detection Software. ELKI, RapidMiner, Shogun, Scikit-learn, Weka, AnomalyDetection are some of the Top Free Anomaly Detection Software.

Top Anomaly Detection Software

Prelert, Anodot, Loom Systems, Interana, Numenta are some of the Top Anomaly Detection Software in no particular order.


Prelert behavioral analytics platform uses machine learning to detect anomalies across massive data sets. The algorithms automate the analysis of an organization’s log data to find anomalies, link them together, and give you real insight into what’s happening with your data.




Anodot is a real time analytics and automated anomaly detection system that discovers outliers in vast amounts of time series data and turns them into valuable business insights. Using patented machine learning algorithms, Anodot isolates issues and correlates them across multiple parameters in real time, eliminating business insight latency, and supporting rapid business decisions through its uncovered insights.



3.Loom Systems

Loom Systems automatically ingests and analyzes all types of logs and metrics, learns their unique behavior over time, detects anomalies and trends, and reports these along with the root cause. The entire cycle is fully automatic, requiring no data pre-processing or manual setting of parameters and thresholds.

Loom Systems

loom systems


Interana is a solution built for behavioral analytics on event data. Interana turn all your disparate event data into an integrated sequence of events to reliably reveal what users truly want, need, or don’t like about your product or service. With Interana, you can quickly develop the right strategies for new opportunities to acquire customers, deepen engagement, and maximize retention.




Numenta, is inspired by machine learning technology is based on a theory of the neocortex. The technology can be applied to anomaly detection in servers and applications, human behavior, and geo-spatial tracking data, and to the predication and classification of natural language. Applications include Detects anomalies in publicly traded companies. Models stock price, stock volume, and Twitter volume related to top market companies.Detects anomalies in servers and applications. Learns continuously, automatically discovers time-based patterns in data, and generalizes from experience


Top Free Anomaly Detection Software

ELKI, RapidMiner, Shogun, Scikit-learn, Weka, AnomalyDetection are some of the Top Free Anomaly Detection Software in no particular order.


ELKI is a university research project with advanced cluster analysis and outlier detection methods written in the Java language.ELKI provides a large collection of highly parameterizable algorithms, in order to allow easy and fair evaluation and benchmarking of algorithms.In ELKI, data mining algorithms and data management tasks are separated and allow for an independent evaluation.





RapidMiner provides an integrated environment for machine learning, data mining, text mining, predictive analytics and business analytics. RapidMiner is used for business, industrial applications, research, education, training, rapid prototyping, and application development and has more than 600 enterprise customers and more than 250,000 active users.




Shogun is a free, open source toolbox written in C++. It offers numerous algorithms and data structures for machine learning problems. The focus of Shogun is on kernel machines such as support vector machines for regression and classification problems. Shogun also offers a full implementation of Hidden Markov models.



Scikit-learn is an open source machine learning library for the Python programming language.It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.



Weka is a suite of machine learning software applications written in the Java programming language. Weka is Waikato Environment for Knowledge Analysis. It is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization.Weka provides access to SQL databases using Java Database Connectivity and can process the result returned by a database query. It is not capable of multi-relational data mining, but there is separate software for converting a collection of linked database tables into a single table that is suitable for processing using Weka.



AnomalyDetection is an open-source R package to detect anomalies which is robust, from a statistical standpoint, in the presence of seasonality and an underlying trend. The AnomalyDetection package can be used in wide variety of contexts. For example, detecting anomalies in system metrics after a new software release, user engagement post an A/B test, or for problems in econometrics, financial engineering, political and social sciences.


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