C4.5算法详解与应用实例

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C4.5算法是机器学习和数据挖掘领域中一种广泛应用的分类算法，专为监督学习设计。该算法旨在接收一个包含属性值的数据集，其中实例由多个属性组成，并且每个实例属于一组互斥类别。C4.5的核心任务是通过学习将属性值映射到类别，以便能够对新的未知实例进行分类。在实际应用中，如图1.1所示，数据集中的行代表特定的日子，而列则表示诸如食物消费等属性，每个条目对应一天的饮食情况，目标是根据这些信息预测当天可能的类别。 1.1 算法介绍 C4.5算法最初由Ross Quinlan提出，基于ID3（Iterative Dichotomiser 3）算法发展而来。它采用决策树作为模型基础，通过信息增益或信息增益比等度量来选择最佳属性划分，以构建最能区分不同类别的树结构。同时，C4.5引入了剪枝技术（Tree Pruning），防止过拟合，提高泛化能力。 1.3 C4.5的特性 1.3.1 **树剪枝**：这是C4.5算法的一个关键改进，通过对生成的决策树进行后剪枝，去除那些在训练数据上表现良好但在测试数据上性能下降的部分，以确保模型的稳定性和实用性。 1.3.2 **连续属性的改进利用**：C4.5可以处理连续属性，通过创建离散化版本或者使用改进的信息增益方法，如Gini指数或基尼不纯度，来决定最佳分割点。 1.3.3 **处理缺失值**：算法提供了策略来处理数据中的缺失值，例如可以选择删除含有缺失值的实例、使用均值或中位数填充，或者利用统计方法推断缺失值。 1.3.4 **规则集诱导**：除了生成单个决策树，C4.5还可以生成一系列规则集，这些规则集合成了更简洁、易于理解的模型，便于用户理解和应用。 1.4 软件实现与讨论 C4.5算法有许多软件实现可供选择，包括Weka、R、Python等工具包，这些工具提供了用户友好的接口，并可能包含算法优化和扩展功能。 1.5 示例应用 1.5.1 **高尔夫数据集**：这个示例展示了如何用C4.5对高尔夫比赛的数据进行分析，可能包括成绩、天气等因素，以预测比赛结果。 1.5.2 **大豆数据集**：另一个案例演示了如何用C4.5分析大豆数据，可能涉及作物生长条件、品种等属性，预测产量或病虫害风险。 1.6 进阶主题 1.6.1 **从二级存储挖掘**：讨论了如何处理大规模数据集，包括数据压缩、索引和分布式计算，以提升C4.5在海量数据上的效率。 1.6.2 **斜向决策树（Oblique Decision Trees）**：这是一种扩展，允许使用非线性组合的特征，增强模型表达能力。 1.6.3 **特征选择**：探讨如何从众多特征中挑选出最有价值的特征，以降低复杂度并提高模型性能。 1.6.4 **集成方法**：介绍了如何通过集成多个C4.5模型（如随机森林）来提高预测准确性和鲁棒性。 1.6.5 **分类规则**：深入研究C4.5生成的规则，以及这些规则如何被解释和应用。 1.6.6 **数据重构（Redescriptions）**：讨论如何通过对原始数据进行变换，简化或重组以提高C4.5的学习效果。 1.7 练习与参考文献本章提供了一系列练习题目，帮助读者巩固对C4.5算法的理解，并推荐了相关的研究文献供进一步探索。总结来说，C4.5算法是机器学习中的一个重要工具，其核心在于构建决策树进行分类，通过剪枝和优化处理连续属性、缺失值等问题，同时支持规则集生成和集成学习方法，适用于多种数据挖掘场景。通过实际案例和进阶主题的学习，读者可以更好地掌握和应用C4.5算法进行数据分析和预测。

16 C4.5

axes. Apply C4.5 on this dataset and comment on the quality of the induced

trees. Take factors such as accuracy, size of the tree, and comprehensibility into

account.

3. An alternative way to avoid overﬁtting is to restrict the growth of the tree rather

than pruning back a fully grown tree down to a reduced size. Explain why such

prepruning may not be a good idea.

4. Prove that the impurity measure used by C45 (i.e., entropy) is concave. Why is

it important that it be concave?

5. Derive Equation (1.1). As stated in the text, use the normal approximation to

the Bernoulli random variable modeling the error rate.

6. Instead of using information gain, study how decision tree induction would be

affectedif we directly selected the attribute with the highest prediction accuracy.

Furthermore, what if we induced rules with only one antecedent? Hint: Yo u

are retracing the experiments of Robert Holte as described in R. Holte, Very

Simple Classiﬁcation Rules Perform Well on Most Commonly Used Datasets,

Machine Learning, vol. 11, pp. 63–91, 1993.

7. In some machine learning applications, attributes are set-valued,for example,an

object can have multiple colors and to classify the object it might be important to

model color as a set-valued attribute rather than as an instance-valued attribute.

Identify decision tests that can be performed on set-valuedattributes and explain

which can be readily incorporated into the C4.5 system for growing decision

trees.

8. Instead of classifying an instance into a single class, assume our goal is to obtain

a ranking of classes according to the (posterior) probability of membership of

the instance in various classes. Read F. Provost and P. Domingos, Tree Induction

for Probability Based Ranking, Machine Learning, vol. 52, no. 3, pp. 199–215,

2003, who explain why the trees induced by C4.5 are not suited to providing

reliable probability estimates; they also suggest some ways to ﬁx this problem

using probability smoothing methods. Do these same objections and solution

strategy apply to C4.5 rules as well? Experiment with datasets from the UCI

repository.

9. (Adapted from S. Nijssen and E. Fromont, Mining Optimal Decision Trees

from Itemset Lattices, Proceedings of the 13th ACM SIGKDD International

Conference on Knowledge Discovery and Data Mining, pp. 530–539, 2007.)

The trees induced by C4.5 are driven by heuristic choices but assume that our

goal is to identify an optimal tree. Optimality can be posed in terms of various

considerations; two such considerations are the most accurate tree up to a

certain maximum depth and the smallest tree in which each leaf covers at least

k instances and the expected accuracy is maximized over unseen examples.

Describe an efﬁcient algorithm to induce such optimal trees.

10. First-order logic is a more expressive notation than the attribute-value repre-

sentation considered in this chapter. Given a collection of ﬁrst-order relations,

describe how the basic algorithmic approach of C4.5 can be generalized to use

References 17

ﬁrst-order features. Your solution must allow the induction of trees or rules of

the form:

grandparent(X,Z) :- parent(X,Y), parent(Y,Z).

that is, X is a grandparent of Z if there exists Y such that Y is the parent of

X and Z is the parent of Y. Several new issues result from the choice of ﬁrst-

order logic as the representational language. First, unlike the attribute value

situation, ﬁrst-order features (such as parent(X,Y)) are not readily given

and must be generalized from the speciﬁc instances. Second, it is possible to

obtain nonsensical trees or rules if the variables participate in the head of a rule

but not the body, for example:

grandparent(X,Y) :- parent(X,Z).

Describe how you can place checks and balances into the induction process

so that a complete ﬁrst-order theory can be induced from data. Hint: You are

exploring the ﬁeld of inductive logic programming [9], speciﬁcally, algorithms

such as FOIL [29].

References

[1] R. Agrawal, T. Imielinski, and A. N. Swami. Mining association rules between

sets of items in large databases. In Proceedings of the ACM SIGMOD Interna-

tional Conference on Management of Data (SIGMOD’93), pp. 207–216, May

1993.

[2] R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large

databases. In Proceedings of the 20th International Conference on Very Large

Databases (VLDB’94), pp. 487–499, Sep. 1994.

[3] A. Asuncion and D. J. Newman. UCI Machine Learning Repository, 2007.

http://www.ics.uci.edu/∼mlearn/MLRepository.html. University of California,

Irvine, School of Information and Computer Sciences.

[4] L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen. Classiﬁcation and

Regression Trees. Chapman & Hall/CRC, Jan. 1984.

[5] C. E. Brodely and P. E. Utgoff. Multivariate Decision Trees. Machine Learning,

19:45–77, 1995.

[6] P. Clark and T. Niblett. The CN2 Induction Algorithm. Machine Learning,

3(4):261–283, 1999.

[7] W. Cohen. Fast Efﬁcient Rule Induction. In Proceedings of the Twelfth Inter-

national Conference on Machine Learning, pp. 115–123, 1995.

18 C4.5

[8] T. G. Dietterich. An Experimental Comparison of Three Methods for Con-

structing Ensembles of Decision Trees: Bagging, Boosting, and Randomiza-

tion. Machine Learning, 40(2):139–157, 2000.

[9] S. Dzeroski and N. Lavrac, eds. Relational Data Mining. Springer, Berlin, 2001.

[10] U. M. Fayyad and K. B. Irani. On the Handling of Continuous-Valued Attributes

in Decision Tree Generation. Machine Learning, 8(1):87–102, Jan. 1992.

[11] Y. Freund and L. Mason. The Alternating Decision Tree Learning Algorithm.

In Proceedings of the Sixteenth International Conference on Machine Learning

(ICML 1999), pp. 124–133, 1999.

[12] Y. Freund and R. E. Schapire. A Short Introduction to Boosting. Journal of the

Japanese Society for Artiﬁcial Intelligence, 14(5):771–780, Sep. 1999.

[13] J. H. Friedman. A Recursive Partitioning Decision Rule for Nonparametric

Classiﬁcation. IEEE Transactions on Computers, 26(4):404–408, Apr. 1977.

[14] J. Gehrke, V. Ganti, R. Ramakrishnan, and W.-H. Loh. BOAT: Optimistic De-

cision Tree Construction. In Proceedings of the ACM SIGMOD International

Conference on Management of Data (SIGMOD’99), pp. 169–180, 1999.

[15] J. Gehrke, R. Ramakrishnan, and V. Ganti. RainForest: A Framework for Fast

Decision Tree Construction of Large Datasets. Data Mining and Knowledge

Discovery, 4(2/3):127–162, 2000.

[16] E. B. Hunt, J. Marin, and P. J. Stone. Experiments in Induction. Academic Press,

New York, 1966.

[17] R. Kohavi, D. Sommerﬁeld, and J. Dougherty. Data Mining Using MLC++: A

Machine Learning Library in C++. In Proceedings of the Eighth International

Conference on Tools with Artiﬁcial Intelligence (ICTAI ’96), pp. 234–245, 1996.

[18] D. Koller and M. Sahami. Toward Optimal Feature Selection. In Proceedings

of the Thirteenth International Conference on Machine Learning (ICML’96),

pp. 284–292, 1996.

[19] D. Kumar, N. Ramakrishnan, R. F. Helm, and M. Potts. Algorithms for Sto-

rytelling. In Proceedings of the Twelfth ACM SIGKDD International Con-

ference on Knowledge Discovery and Data Mining (KDD’06), pp. 604–610,

Aug. 2006.

[20] B. Liu, W. Hsu, and Y. Ma. Integrating Classiﬁcation and Association Rule

Mining. In Proceedings of the Fourth International Conference on Knowledge

Discovery and Data Mining (KDD’98), pp. 80–86, Aug. 1998.

[21] M. Mehta, R. Agrawal, and J. Rissanen. SLIQ: A Fast Scalable Classiﬁer for

Data Mining. In Proceedings of the 5th International Conference on Extending

Database Technology (EDBT’96), pp. 18–32, Mar. 1996.

[22] S. K. Murthy, S. Kasif, and S. Salzberg. A System for Induction of Oblique

Decision Trees. Journal of Artiﬁcial Intelligence Research, 2:1–32, 1994.

References 19

[23] D.W. Opitz and R. Maclin. Popular Ensemble Methods: An Empirical Study.

Journal of Artiﬁcial Intelligence Research, 11:169–198, 1999.

[24] L. Parida and N. Ramakrishnan. Redescription Mining: Structure Theory and

Algorithms. In Proceedings of the Twentieth National Conference on Artiﬁcial

Intelligence (AAAI’05), pp. 837–844, July 2005.

[25] J. R. Quinlan. Induction of Decision Trees. Machine Learning, 1(1):81–106,

1986.

[26] J. R. Quinlan. Simplifying Decision Trees. Technical Report 930, MIT AI Lab

Memo, Dec. 1986.

[27] J. R. Quinlan. Decision Trees as Probabilistic Classiﬁers. In P. Langley, ed.,

Proceedings of the Fourth International Workshop on Machine Learning. Mor-

gan Kaufmann, CA, 1987.

[28] J. R. Quinlan. Unknown Attribute Values in Induction. Technical report, Basser

Department of Computer Science, University of Sydney, 1989.

[29] J. R. Quinlan. Learning Logical Deﬁnitions from Relations. Machine Learning,

5:239–266, 1990.

[30] J.R. Quinlan. C4.5: Programsfor MachineLearning. MorganKaufmann, 1993.

[31] J. R. Quinlan. Improved Use of Continuous Attributes in C4.5. Journal of

Artiﬁcial Intelligence Research, 4:77–90, 1996.

[32] N. Ramakrishnan, D. Kumar, B. Mishra, M. Potts, and R. F. Helm. Turning

CARTwheels: An Alternating Algorithm for Mining Redescriptions. In Pro-

ceedings of the Tenth ACM SIGKDD International Conference on Knowledge

Discovery and Data Mining (KDD’04), pp. 266–275, Aug. 2004.

[33] R. Rastogi and K. Shim. PUBLIC: A Decision Tree Classiﬁer that Integrates

Building and Pruning. In Proceedings of the 24th International Conference on

Very Large Data Bases (VLDB’98), pp. 404–415, Aug. 1998.

[34] J. C. Shafer, R. Agrawal, and M. Mehta. SPRINT: A Scalable Parallel Classiﬁer

for Data Mining. In Proceedings of the 22th International Conference on Very

Large Data Bases (VLDB’96), pp. 544–555, Sep. 1996.

[35] I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and

Techniques. Morgan Kaufmann, 2005.

[36] X.Wu, V.Kumar, J. R. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. J. McLachlan,

A. Ng, B. Liu, P. S. Yu, Z.-H. Zhou, M. Steinbach, D. J. Hand, and D. Steinberg.

Top 10 Algorithms in Data Mining. Knowledge and Information Systems,

14:1–37, 2008.

[37] L. Zhao, M. Zaki, and N. Ramakrishnan. BLOSOM: A Framework for Mining

Arbitrary Boolean Expressions over Attribute Sets. In Proceedings of the

Twelfth ACM SIGKDD International Conference on Knowledge Discovery and

Data Mining (KDD’06), pp. 827–832, Aug. 2006.

Chapter 2

K-Means

Joydeep Ghosh and Alexander Liu

Contents

2.1 Introduction ............................................................ 21

2.2 The k-means Algorithm ................................................. 22

2.3 Available Software ...................................................... 26

2.4 Examples ............................................................... 27

2.5 Advanced Topics ........................................................ 30

2.6 Summary ............................................................... 32

2.7 Exercises ............................................................... 33

References ................................................................... 34

2.1 Introduction

In this chapter, we describe the k-means algorithm, a straightforward and widely

used clustering algorithm. Given a set of objects (records), the goal of clustering

or segmentation is to divide these objects into groups or “clusters” such that objects

withina grouptendto bemore similartoone anotherascompared toobjects belonging

to different groups. In other words, clustering algorithms place similar points in the

same cluster while placing dissimilar points in different clusters. Note that, in contrast

to supervised tasks such as regression or classiﬁcation where there is a notion of a

target value or class label, the objects that form the inputs to a clustering procedure

do not come with an associated target. Therefore, clustering is often referred to

as unsupervised learning. Because there is no need for labeled data, unsupervised

algorithms are suitable for many applications where labeled data is difﬁcult to obtain.

Unsupervised tasks such as clustering are also often used to explore and characterize

the dataset before running a supervised learning task. Since clustering makes no use

of class labels, some notion of similarity must be deﬁned based on the attributesof the

objects.The deﬁnition ofsimilarityand themethodin which pointsare clustered differ

based on the clustering algorithm being applied. Thus, different clustering algorithms

are suited to different types of datasets and different purposes. The “best” clustering

algorithm to use therefore depends on the application. It is not uncommon to try

several different algorithms and choose depending on which is the most useful.

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