Mining Data Streams Using Option Trees

نویسندگان

  • Geoffrey Holmes
  • Richard Kirkby
  • Bernhard Pfahringer
چکیده

The data stream model for data mining places harsh restrictions on a learning algorithm. A model must be induced following the briefest interrogation of the data, must use only available memory and must update itself over time within these constraints. Additionally, the model must be able to be used for data mining at any point in time. This paper describes a data stream classification algorithm using an ensemble of option trees. The ensemble of trees is induced by boosting and iteratively combined into a single interpretable model. The algorithm is evaluated using benchmark datasets for accuracy against state-of-the-art algorithms that make use of the entire dataset.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Modeling dynamical systems with data stream mining

We address the task of modeling dynamical systems in discrete time using regression trees, model trees and option trees for on-line regression. Some challenges that modeling dynamical systems pose to data mining approaches are described: these motivate the use of methods for mining data streams. The algorithm FIMT-DD for mining data streams with regression or model trees is described, as well a...

متن کامل

Mining frequent closed trees in evolving data streams

We propose new algorithms for adaptively mining closed rooted trees, both labeled and unlabeled, from data streams that change over time. Closed patterns are powerful representatives of frequent patterns, since they eliminate redundant information. Our approach is based on an advantageous representation of trees and a low-complexity notion of relaxed closed trees, as well as ideas from Galois L...

متن کامل

Mining Frequent Patterns in Uncertain and Relational Data Streams using the Landmark Windows

Todays, in many modern applications, we search for frequent and repeating patterns in the analyzed data sets. In this search, we look for patterns that frequently appear in data set and mark them as frequent patterns to enable users to make decisions based on these discoveries. Most algorithms presented in the context of data stream mining and frequent pattern detection, work either on uncertai...

متن کامل

Mining Decision Trees from Data Streams in a Mobile Environment

This paper presents a novel Fourier analysis-based technique to aggregate, communicate, and visualize decision trees in a mobile environment. Fourier representation of a decision tree has several useful properties that are particularly useful for mining continuous data streams from small mobile computing devices. This paper presents algorithms to compute the Fourier spectrum of a decision tree ...

متن کامل

Orthogonal Decision Trees for Resource-Constrained Physiological Data Stream Monitoring Using Mobile Devices

Several challenging new applications demand the ability to do data mining on resource constrained devices. One such application is that of monitoring physiological data streams obtained from wearable sensing devices. Such monitoring has applications for pervasive healthcare management, be it for seniors, emergency response personnel, soldiers in the battlefield or athletes. A key requirement is...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2004