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About 10,000 papers for “data mining”

Stayuptildawn/data-mining-pipeline: data-mining-pipeline

Mohammad Soleimani Roudi · Zenodo (CERN European Organization for Nuclear Research) · 2026

General-purpose time series forecasting pipeline — baselines, ARIMA, Gradient Boosting, and LSTM in a single configurable Python script.

Cited by 0Open access

Stayuptildawn/data-mining-pipeline: data-mining-pipeline

Mohammad Soleimani Roudi · Zenodo (CERN European Organization for Nuclear Research) · 2026

General-purpose time series forecasting pipeline — baselines, ARIMA, Gradient Boosting, and LSTM in a single configurable Python script.

Cited by 0Open access

A. Data Mining And Stream Data Mining

Mistry Vinay R, Ms. Astha Baxi · 2015

Abstract- The rapid development in the e-commerce and distributed computing generates millions of the transaction, continuously. This continues arrival of data is considered as a DataStream. Data mining process for classification needs considerable modification to cope with continuous data. As Mining continues stream of data, conceptually has infinite length, and the class of data may change in sudden or gradual or hike, for which classification model is completely unknown or not prepared. Here investigation is made on different techniques proposed for the data stream classification using decision trees. Different approaches of decision tree classification for the stream data are analyzed &amp; compared. The primary comparison parameters are time and accuracy. Also shown efforts made for handling the change in the concept and they are compared in terms of memory, technique and accuracy.

Cited by 0

Data mining with Oracle Data Mining (11g)

Grega Boštjančič · 2010

Data mining is becoming more and more important on number of fields in our lives. It is used by doctors defining medical conditions, bankers dealing with risk management and so on. It is a process of discovering information, patterns and correlation with searching through bigger or smaller quantity of data. Every data mining process, if one wants it to be successful, must be composed from exact steps or tasks. To reach our goal, we often have to take a step back to improve the former state. This process is standardizated with standard CRISP-DM (CRoss-Industry Standard Process for Data Mining). Data mining uses algorithms for pattern discovering as well as statistical and mathematical techniques. There are many applications which use sufisticated mix of classic and advanced algorihtms like decision tree, Naive Bayes, Support Vector Machines etc. In this paper I compared methods and scalability in two applications for data mining. The first is included in Oracle database, Oracle Data Miner, whereas the second, Weka, is independent, opensource java application. It turns out that Oracle is better with working with larger datasets while Weka is more accurate with smaller datasets. Weka's problem is mainly her wastefulness with system sources which makes her incompetent at working with large datasets. Orcale, however, sacrifaces accuracy in order to be capable of always building a model.

Cited by 0

Data Mining: Data Mining Concepts and Techniques

Shivam Agarwal · 2013

Data mining is a field of intersection of computer science and statistics used to discover patterns in the information bank. The main aim of the data mining process is to extract the useful information from the dossier of data and mold it into an understandable structure for future use. There are different process and techniques used to carry out data mining successfully.

Cited by 280

Data Mining

Elsevier eBooks · 2012

Cited by 342

Data mining

Terry Ngo · ACM SIGSOFT Software Engineering Notes · 2011

No abstract available.

Cited by 39

Data Mining

U Kang, Advisor Professor, Christos Faloutsos et al. · 2012

RESEARCH INTERESTS Data mining in massive graphs, with an emphasis on bridging graph mining and systems techniques for extremely scalable data analysis. Specifically: distributed mining and managing billion-scale graphs, graph indexing, graph compression, spectral graph analysis, tensor, anomaly detection, modeling evolution, and inference in graphs.

Cited by 0

Data mining

Daniel T. Larose, Chantal D. Larose · Vuibert eBooks · 2018

Cited by 0

Data Mining

Mehmed Kantardzic · 2009

A comprehensive introduction to the exploding field of data mining We are surrounded by data, numerical and otherwise, which must be analyzed and processed to convert it into information that informs, instructs, answers, or otherwise aids understanding and decision-making. Due to the ever-increasing complexity and size of today's data sets, a new term, data mining, was created to describe the indirect, automatic data analysis techniques that utilize more complex and sophisticated tools than those which analysts used in the past to do mere data analysis. Data Mining: Concepts, Models, Methods, and Algorithms discusses data mining principles and then describes representative state-of-the-art methods and algorithms originating from different disciplines such as statistics, machine learning, neural networks, fuzzy logic, and evolutionary computation. Detailed algorithms are provided with necessary explanations and illustrative examples. This text offers guidance: how and when to use a particular software tool (with their companion data sets) from among the hundreds offered when faced with a data set to mine. This allows analysts to create and perform their own data mining experiments using their knowledge of the methodologies and techniques provided. This book emphasizes the selection of appropriate methodologies and data analysis software, as well as parameter tuning. These critically important, qualitative decisions can only be made with the deeper understanding of parameter meaning and its role in the technique that is offered here. Data mining is an exploding field and this book offers much-needed guidance to selecting among the numerous analysis programs that are available

Cited by 46

Data Mining

Nicolas Bissantz, Jürgen Hagedorn, Peter Mertens · Gabler Verlag eBooks · 2000

Früher war der Begriff “Management Information System” mit “Zahlenfriedhof” nahezu gleichzusetzen. Heute stehen MIS- oder EIS-Tools1 zur Verfügung, die es erlauben, ganz unterschiedliche Sichten auf Datenbestände zu nehmen, ein Fenster nach dem anderen zu öffnen und zu schließen, in viele Richtungen zu navigieren, Informationen nach Wunsch zu verdichten, Tabellen und Grafiken immer wieder anders zu gestalten. Oft ist der Zeitbedarf hierfür nicht viel geringer als der, den man früher brauchte, um sich durch die ausgedruckten Papierstapel mit den vielen Fakten hindurchzuarbeiten. Kennzeichnend für die heutige Situation sind Worte wie “Dialogorgie” oder “MUFF” (Maus- und Fenster-Firlefanz).

Cited by 10

Data mining

Matthias Meyer · WIRTSCHAFTSINFORMATIK · 2006

Cited by 2

Data Mining

David J. Hand, Niall M. Adams · Wiley StatsRef: Statistics Reference Online · 2015

Abstract Data mining is the discovery of structures and patterns in large and complex data sets. There are two aspects to data mining: model building and pattern detection. Model building in data mining is very similar to statistical modeling, although new problems arise because of the large sizes of the data sets and the fact that data mining is often secondary data analysis. Pattern detection seeks anomalies or small local structures in data, with the vast mass of the data being irrelevant. Indeed, one view of many large‐scale data mining activities is that they primarily constitute filtering and data reduction. Although some subdisciplines of statistics have examined special cases of this problem, the bulk of the work on pattern detection to date has been computational, with an emphasis on algorithms.

Cited by 28

Cited by 3