adatkereshetséget

Adatkereséség, meaning data mining or knowledge discovery in databases (KDD), is the process of extracting novel, interesting, and ultimately useful patterns from large datasets. It is an interdisciplinary field that draws upon methods from statistics, machine learning, artificial intelligence, and database systems. The goal of adatkereséség is to transform raw data into understandable and actionable insights.

The adatkereséség process typically involves several stages. First, data understanding is crucial to comprehend the domain and the available data. This is followed by data preparation, which includes data cleaning, integration, transformation, and reduction to make the data suitable for mining. The core of adatkereséség is the data mining step itself, where algorithms are applied to discover patterns. These patterns are then evaluated for their interestingness and usefulness. Finally, the discovered knowledge is presented and deployed.

Common techniques used in adatkereséség include classification, clustering, association rule mining, and anomaly detection. Classification aims to assign items to predefined categories. Clustering groups similar items together without prior knowledge of the categories. Association rule mining finds relationships between items in a dataset, such as "customers who buy bread also tend to buy milk." Anomaly detection identifies unusual data points that deviate significantly from the norm.

Adatkereséség has applications across various domains, including business intelligence, marketing, finance, healthcare, and scientific research. It helps organizations make better decisions, understand customer behavior, detect fraud, diagnose diseases, and advance scientific understanding by uncovering hidden trends and correlations within their data.