Data mining, also known as Knowledge Discovery in Data (KDD), is the process of uncovering patterns and other valuable information from large data sets. Data mining has improved organizational decision-making through insightful data analyses. The data mining techniques are used to organize and filter data, surfacing the most interesting information, from fraud detection to user behaviors, bottlenecks, and even security breaches.
When combined with data analytics and visualization tools, like Apache Spark, delving into the world of data mining has never been easier and extracting relevant insights has never been faster. Advances within artificial intelligence only continue to expedite adoption across industries.
Data mining techniques
Data mining works by using various algorithms and techniques to turn large volumes of data into useful information. Here are some of the most common ones:
Association rules: An association rule is a rule-based method for finding relationships between variables in a given dataset. These methods are frequently used for market basket analysis, allowing companies to better understand relationships between different products.
Neural networks: Primarily leveraged for deep learning algorithms, neural networks process training data by mimicking the interconnectivity of the human brain through layers of nodes. Each node is made up of inputs, weights, a bias (or threshold), and an output. If that output value exceeds a given threshold, it “fires” or activates the node, passing data to the next layer in the network.
Decision tree: This data mining technique uses classification or regression methods to classify or predict potential outcomes based on a set of decisions. As the name suggests, it uses a tree-like visualization to represent the potential outcomes of these decisions.
K- nearest neighbor (KNN): K-nearest neighbor, also known as the KNN algorithm, is a non-parametric algorithm that classifies data points based on their proximity and association to other available data. This algorithm assumes that similar data points can be found near each other. As a result, it seeks to calculate the distance between data points, usually through Euclidean distance, and then it assigns a category based on the most frequent category or average.
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