Identifying and Characterising Anomalies in Data (original) (raw)

Detection of Anomalous Value in Data Mining

Kalpa Publications in Engineering

In the database of numeric values, outliers are the points which are different from other values or inconsistent with the rest of the data. They can be novel, abnormal, unusual or noisy information. Outliers are more attention-grabbing than the high proportion data. The challenges of outlier detection arise with the increasing complexity, mass and variety of datasets. The problem is how to manage outliers in a dataset, and how to evaluate the outliers. This paper describes an advancement of approach which uses outlier detection as a pre-processing step to detect the outlier and then applies rectangle fit algorithm, hence to analyze the effects of the outliers on the analysis of dataset.

A Comparative Study for Anomaly Detection in Data Mining

2008

In this paper, we will discuss some of the research we have found till and what we have concluded from that survey. We try to compare and combine three of the methods we have explored. We will work on Outlier / Anomaly Detection. Data mining is the process of extraction of data that would be of any kind and Outlier / Anomaly is detection of irrelevant data.

DATA MINING: A CONCEPTUAL OVERVIEW

This tutorial provides an overview of the data mining process. The tutorial also provides a basic understanding of how to plan, evaluate and successfully refine a data mining project, particularly in terms of model building and model evaluation. Methodological considerations are discussed and illustrated. After explaining the nature of data mining and its importance in business, the tutorial describes the underlying machine learning and statistical techniques involved. It describes the CRISP-DM standard now being used in industry as the standard for a technology-neutral data mining process model. The paper concludes with a major illustration of the data mining process methodology and the unsolved problems that offer opportunities for research. The approach is both practical and conceptually sound in order to be useful to both academics and practitioners.

Data Mining for Anomaly Detection

Tutorial at the European Conference on Principles and Practice of Knowledge Discovery in Databases, Antwerp, Belgium, September, 2008

Data Mining Fundamental Concepts and Critical Issues

Encyclopedia of Artificial Intelligence

Data mining is the process of extracting previously unknown information from large databases or data warehouses and using it to make crucial business decisions. Data mining tools find patterns in the data and infer rules from them. The extracted information can be used to form a prediction or classification model, identify relations between database records, or provide a summary of the databases being mined. Those patterns and rules can be used to guide decision making and forecast the effect of those decisions, and data mining can speed analysis by focusing attention on the most important variables.

Finding the Missing Data to Detect Patterns using Data Mining

Data mining refers to the extracting or " mining " knowledge from large amount of data. The process of performing data analysis may uncover important data patterns, contributing greatly to business strategies, knowledge bases, and scientific and medical research. The exploration and analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns and rules. The rules include the iterative process of detecting and extracting patterns from large databases. This paper helps us to identify " signatures " hidden in large databases, as well as learn from repeated examples. The extraction of implicit, previously unknown, and potentially useful information from data is the ultimate goal of any statically viable approach. Strong patterns, if found, will likely generalize to make accurate predictions on future data. Data Mining automates the detection of relevant patterns in databases. The Pattern finding is applied for disguised bank details and complete result is expected.

Data Mining Techniques in Database Systems

2017

At the current stage the technologies for generating and collecting data have been advancing rapidly. The main problem is the extraction of valuable and accurate information from large data sets. One of the main techniques for solving this problem is Data Mining. Data mining (DM) is the process of identification and extraction of useful information in typically large databases. DM aims to automatically discover the knowledge that is not easily perceivable. It uses statistical analysis and artificial intelligence (AI) techniques together to address the issues. There are different types of tasks associated to data mining process. Each task can be thought of as a particular kind of problem to be solved by a data mining algorithm. The main types of tasks performed by DM algorithms are: Classification, Association, Clustering, Regression, Anomaly Detection, Feature Extraction, Time Series Analyses.

Implementation of Anomaly Detection Using Data Mining Technique

The Purpose of data mining is extracting vital information from huge databases or the data warehouses. Many Data mining applications have used for commercial & scientific sides. This type of study emphatically discusses Data Mining applications into scientific side. Here Scientific data mining differentiates itself and explores that nature of datasets is various from present market concentrated data mining applications. Most people use pattern matching in some form. Search engines on Web use pattern matching to locate information of interest.

Data mining: as an imperative tool for discovering knowledge

Computational intelligence, 2007

Data mining and knowledge discovery in databases have been attracting a foremost amount of research, industry, and media attention of late. What is all the excitement about? This paper provides an overview of this emerging field, clarifying how data mining and knowledge discovery in databases are related both to each other. The paper mentions particular realworld applications, specific data mining techniques, challenges involved in real world applications of knowledge discovery, and current and future research directions in the field.

A Conceptual Overview of Data Mining

—Data mining an non-trivial extraction of novel, implicit, and actionable knowledge from large data sets is an evolving technology which is a direct result of the increasing use of computer databases in order to store and retrieve information effectively .It is also known as Knowledge Discovery in Databases (KDD) and enables data exploration, data analysis, and data visualization of huge databases at a high level of abstraction, without a specific hypothesis in mind. The working of data mining is understood by using a method called modeling with it to make predictions. Data mining techniques are results of long process of research and product development and include artificial neural networks, decision trees and genetic algorithms. This paper surveys the data mining technology, its definition, motivation, its process and architecture, kind of data mined, functionalities and classification of data mining, major issues, applications and directions for further research of data mining technology.