聚类算法

Feng-Jyh Lin
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引用次数: 0

摘要

数据挖掘任务—预测性任务与描述性任务—预测性任务:根据其他属性的值预测特定属性(目标变量)的值—描述性任务:导出模式(相关性,趋势,集群,轨迹和异常),总结数据中的潜在关系·预测建模-预测建模的目标是学习一个模型,最大限度地减少目标变量的预测值和真实值之间的误差-分类:对于离散目标变量-回归:对于连续目标变量-聚类分析-目标是找到相似的观察/对象组-许多聚类算法已经开发
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Clustering Algorithms
Data mining tasks  Predictive vs descriptive tasks – Predictive tasks: predict the value of a particular attribute (the target variable) based on the values of other attributes – Descriptive tasks: derive patterns (correlations, trends, clusters, trajectories, and anomalies) that summarize the underlying relationships in data  Predictive modeling – The goal of predictive modeling is to learn a model that minimizes the error between the predicted and true values of the target variable – Classification: for discrete target variables – Regression: for continuous target variables  Cluster analysis – The goal is to find groups of similar observations/objects – Many clustering algorithms have been developed Applications of clustering in biology
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