Machine Learning

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What is data mining?

Data mining is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. In the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.

In the early stage for identifying patterns in data we use Bayes' theorem and regression analysis. But as data sets have grown in size and complexity discoveries in computer science such as neural networks, cluster analysis, genetic algorithms , decision trees and decision rules , and support vector machines has augmented with indirect, automated data processing.

so in data mining ,we apply these methods with the intention of uncovering hidden patterns in large data sets.

Data mining involves six common classes of tasks:

• Anomaly detection (outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation.

• Association rule learning (dependency modeling) – Searches for relationships between variables. For example, a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.

• Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.

• Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam".

• Regression – attempts to find a function that models the data with the least error that is, for estimating the relationships among data or datasets.

• Summarization – providing a more compact representation of the data set, including visualization and report generation.

Data mining software and applications:

• KNIME: The Konstanz Information Miner, a user-friendly and comprehensive data analytics framework.

• NLTK (Natural Language Toolkit): A suite of libraries and programs for symbolic and statistical natural language processing (NLP) for the Python language.

• scikit-learn is an open-source machine learning library for the Python programming language.

• Torch: An open-source deep learning library for the Lua programming language and scientific computing framework with wide support for machine learning algorithms.

• Weka: A suite of machine learning software applications written in the Java programming language.

• RapidMiner: An environment for machine learning and data mining experiments.

Data mining Methods used to solve assignment

• Agent mining

• Anomaly/outlier/change detection

• Association rule learning

• Bayesian networks

• Classification

• Cluster analysis

• Decision trees

• Ensemble learning

• Factor analysis

• Genetic algorithms

• Intention mining

• Learning classifier system

• Multilinear subspace learning

• Neural networks

• Regression analysis

• Sequence mining

• Structured data analysis

• Support vector machines

• Text mining

• Time series analysis.