Apache Mahout is a new open source project by the Apache Software Foundation (ASF) with the primary goal of creating highly scalable machine-learning algorithms that are fast and free to use under the Apache license. Mahout’s core algorithms for clustering, classification, and batch-based collaborative filtering are implemented on top of Apache Hadoop using the Mapreduce paradigm. Currently, Mahout supports mainly three common machine-learning use cases: (1) user-based recommendations, where data is mined using known user preferences and behaviours and used to predict new preferences for the user (there is also limited support for the related approach, item-based recommendations), (2) clustering looks for similarities between data points, using a user-specified metric, to identify clusters in the data, that is groups of points that appear more similar to each other than to members of other groups, and (3) classification applies discrete labels to data or predicts a continuous value (e.g., a price) based on previous examples of similar data.
Apache Mahout is Free and open source project. It is a library of scalable machine learning Algorithms, implemented on top of Apache Hadoop and using the Map reduce paradigm. It implements the most popular machine learning techniques like recommendation, classification, clustering, and collaborative filtering. In this Mahout contains manly java libraries for common math algorithms and different operations like or, and, not focused on statics and linear algebra as well as primitive java collection. It also provides the data science tools to automatically find interesting patterns in those big data sets. Most companies used in mahout internally: Facebook, LinkedIn, Foursquare, twitter, yahoo, adobe etc. The Mahout project was started by several people involved in the Apache Lucene (open source search) community with an active interest in machine learning and a desire for robust, well-documented, scalable implementations of common machine-learning algorithms for clustering and categorization. The community was initially driven by Ng et al.’s paper “Map-Reduce for Machine Learning on Multicore” (see Resources) but has since evolved to cover much broader machine-learning approaches. Mahout also aims to: Mahout supports four main data science use cases: Collaborative filtering: It mines user behaviour and makes product recommendations (Amazon recommendations) Clustering: It takes items in a particular class (such as web pages or newspaper articles) and organizes them into naturally occurring groups, such that items belonging to the same group are similar to each other Classification: learns from existing categorizations and then assigns unclassified items to the best category Frequent item-set mining: analyzes items in a group (e.g. items in a shopping cart or terms in a query session) and then identifies which items typically appear together Clustering is the procedure to organize elements of a given data collection into groups based on the similarity between the items. Or Clustering is grouping any forms of data into characteristically similar groups of data sets. Mahout supports many different clustering mechanisms. The important clustering mechanisms are Unless you are highly proficient in java, the coding itself is a big overhead. There’s no way around it. If you don’t know it’s already you are going to need to learn java and its not language that flows! For R users who are used to seeing their thoughts realized immediately the endless declaration and initialization of objects is going to seem like a drag. For that reason I would recommend sticking with R for any kind of data exploration or prototyping and switching to Mahout as you get closer to production. Recommendation engine is a subset of information filtering systems which can predict the rating or preferences user can give to an item. Using taste library we can build a fast algorithm and flexible collaborative filtering engine. Below are primary components of taste library Mahout is Hadoop Map reduce and MLib is spark .To be more specific from the difference in per job overhead .If Your ML algorithm mapped to the single MR job – main difference will be only start-up overhead, which is dozens of seconds for Hadoop MR, and let say 1 second for Spark. So in case of model training it is not that important. Things will be different if your algorithm is mapped to many jobs. In this case we will have the same difference on overhead per iteration and it can be game changer.For example: we need 100 iterations; each needed 5 seconds of cluster CPU. On Hadoop: MR (Mahout) it will take 100*5+100*30 = 3500 seconds. On Spark: it will take 100*5 + 100*1 seconds = 600 seconds. In the same time Hadoop MR is much more mature framework then Spark and if you have a lot of data, and stability is paramount – I would consider Mahout as serious alternative. Commercial Use: Academic UseWhat is Apache Mahout?
What are the features of Apache Mahout?
Can you briefly explain the Apache Mahout?
What does Apache Mahout do?
Can you explain Clustering in Mahout?
Can you explain how it is different from doing machine learning in R or SAS?
Can you explain Recommendation engine?
What are the machine learning algorithms supports in Apache Mahout?
Can you explain difference between Apache Mahout and Apache Spark’s MLlib?
Mention Some Use Cases Of Apache Mahout?
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