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Typically data is discretized into partitions of K equal lengths/width (equal intervals) or K% of the total data (equal frequencies). [1] Mechanisms for discretizing continuous data include Fayyad & Irani's MDL method, [2] which uses mutual information to recursively define the best bins, CAIM, CACC, Ameva, and many others [3]
In 2007 the technique was being used in a few data warehouses and one online transaction processing (OLTP) system, and it was presented internationally by Lars Rönnbäck at the 2007 Transforming Data with Intelligence (TDWI) conference in Amsterdam. [4] This stirred enough interest for the technique to warrant a more formal description.
On the other hand, it enables non-experts, who are not familiar with data science, to quickly extract value from their data with a little effort, time, and cost. [22] getML community is an open source tool for automated feature engineering on time series and relational data. [23] [24] It is implemented in C/C++ with a Python interface. [24]
Interactive data transformation (IDT) [13] is an emerging capability that allows business analysts and business users the ability to directly interact with large datasets through a visual interface, [9] understand the characteristics of the data (via automated data profiling or visualization), and change or correct the data through simple ...
The last step in data modeling is transforming the logical data model to a physical data model that organizes the data into tables, and accounts for access, performance and storage details. Data modeling defines not just data elements, but also their structures and the relationships between them.
JAX is a machine learning framework for transforming numerical functions. [2] [3] [4] It is described as bringing together a modified version of autograd (automatic obtaining of the gradient function through differentiation of a function) and OpenXLA's XLA (Accelerated Linear Algebra).
Intelligent transformation is the process of deriving better business and societal outcomes by leveraging smart devices, big data, artificial intelligence, and cloud technologies. Intelligent transformation can facilitate firms in gaining recognition from external investors, thereby enhancing their market image and attracting larger consumers ...
Data wrangling, sometimes referred to as data munging, is the process of transforming and mapping data from one "raw" data form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics. The goal of data wrangling is to assure quality and useful data.