Data reduction strategies in data mining pdf

Data reduction is a capacity optimization technique in which data is reduced to its simplest possible form to free up capacity on a storage device. There are many ways to reduce data, but the idea is very simple—squeeze as much data into physical storage as possible to maximize capacity. In this article, we'll dive into the basics of data Many real-world data mining tasks involve continuous attributes. However, many of the existing data mining systems cannot han-dle such attributes. Furthermore, even if a data mining task can handle a continuous attribute its performance can be signifi cantly improved by replacing a continuous attribute with its discretized values. Mining of relational databases search the trends and data patterns E.g. credit risk of customers based on age, income, and previous credit risk.Also, mining can find out deviations from the expected E.g. a significant increase in the price of an item. #2) Data Warehouse Data: A data warehouse is a collection of information collected from multiple data sources, stored under a unified schema at Data mining is the process that helps all organizations detect patterns and develop insights as per the business requirements. Plenty of methods help every organization convert raw data into actionable insights for improving company growth. Some of the most widely used methods in data mining are: 1. Data cleaning. Data Mining Process. After understanding the data mining definition, let's understand the data mining process.Before the actual data mining could occur, there are several processes involved in data mining implementation.Here's how: Step 1: Business Research - Before you begin, you need to have a complete understanding of your enterprise's objectives, available resources, and current Data reduction is a method of reducing the volume of data thereby maintaining the integrity of the data. There are three basic methods of data reduction dimensionality reduction, numerosity reduction and data compression. The time taken for data reduction must not be overweighed by the time preserved by data mining on the reduced data set. Dimensionality reduction is an effective approach to collect less data but efficient data. Dimensionality Reduction is very helpful in the projection of high-dimensional data onto 2D or 3D Visualization. Dimensionality Reduction is helpful in inefficient storage and retrieval of the data and promotes the concept of Data compression. Dimensionality Reduction encourages the positive effect on query accuracy by Noise removal. Data reduction can be obtained by assuming a statistical model for the data. Classical principles of data reduction include sufficiency, likelihood, conditionality and equivariance. [5] See also [ edit] Data cleansing Data editing Data pre-processing Data wrangling References [ edit] ^ "Travel Time Data Collection Handbook" (PDF). Data mining automatically extracts hidden and intrinsic information from the collections of data. Data mining has various techniques that are suitable for data cleaning. Understanding and correcting the quality of your data is imperative in getting to an accurate final analysis. The data needs to be prepared to discover crucial patterns. Data mining is considered exploratory. Data cleaning in data mining allows the user to discover inaccurate or incomplete data before the business analysis 1. Training: A model is learne