Induction of multiclass multifeature split decision trees from distributed data

The decision tree-based classification is a popular approach for pattern recognition and data mining. Most decision tree induction methods assume training data being present at one central location. Given the growth in distributed databases at geographically dispersed locations, the methods for decision tree induction in distributed settings are gaining importance. This paper describes one such method that generates compact trees using multifeature splits in place of single feature split decision trees generated by most existing methods for distributed data. Our method is based on Fisher's linear discriminant function, and is capable of dealing with multiple classes in the data. For homogeneously distributed data, the decision trees produced by our method are identical to decision trees generated using Fisher's linear discriminant function with centrally stored data. For heterogeneously distributed data, a certain approximation is involved with a small change in performance with respect to the tree generated with centrally stored data. Experimental results for several well-known datasets are presented and compared with decision trees generated using Fisher's linear discriminant function with centrally stored data.     

Orthogonal decision trees

This paper introduces orthogonal decision trees that offer an effective way to construct a redundancy-free, accurate, and meaningful representation of large decision-tree-ensembles often created by popular techniques such as bagging, boosting, random forests, and many distributed and data stream mining algorithms. Orthogonal decision trees are functionally orthogonal to each other and they correspond to the principal components of the underlying function space. This paper offers a technique to construct such trees based on the Fourier transformation of decision trees and eigen-analysis of the ensemble in the Fourier representation. It offers experimental results to document the performance of orthogonal trees on the grounds of accuracy and model complexity.     

Mining unexpected patterns using decision trees and interestingness measures: a case study of endometriosis

Because clinical research is carried out in complex environments, prior domain knowledge, constraints, and expert knowledge can enhance the capabilities and performance of data mining. In this paper we propose an unexpected pattern mining model that uses decision trees to compare recovery rates of two different treatments, and to find patterns that contrast with the prior knowledge of domain users. In the proposed model we define interestingness measures to determine whether the patterns found are interesting to the domain. By applying the concept of domain-driven data mining, we repeatedly utilize decision trees and interestingness measures in a closed-loop, in-depth mining process to find unexpected and interesting patterns. We use retrospective data from transvaginal ultrasound-guided aspirations to show that the proposed model can successfully compare different treatments using a decision tree, which is a new usage of that tool. We believe that unexpected, interesting patterns may provide clinical researchers with different perspectives for future research.     

A data mining proxy approach for efficient frequent itemset mining

Data mining has attracted a lot of research efforts during the past decade. However, little work has been reported on the efficiency of supporting a large number of users who issue different data mining queries periodically when there are new needs and when data is updated. Our work is motivated by the fact that the pattern-growth method is one of the most efficient methods for frequent pattern mining which constructs an initial tree and mines frequent patterns on top of the tree. In this paper, we present a data mining proxy approach that can reduce the I/O costs to construct an initial tree by utilizing the trees that have already been resident in memory. The tree we construct is the smallest for a given data mining query. In addition, our proxy approach can also reduce CPU cost in mining patterns, because the cost of mining relies on the sizes of trees. The focus of the work is to construct an initial tree efficiently. We propose three tree operations to construct a tree. With a unique coding scheme, we can efficiently project subtrees from on-disk trees or in-memory trees. Our performance study indicated that the data mining proxy significantly reduces the I/O cost to construct trees and CPU cost to mine patterns over the trees constructed.     

Parallel Formulations of Decision-Tree Classification Algorithms

Classification decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud detection, etc. Highly parallel algorithms for constructing classification decision trees are desirable for dealing with large data sets in reasonable amount of time. Algorithms for building classification decision trees have a natural concurrency, but are difficult to parallelize due to the inherent dynamic nature of the computation. In this paper, we present parallel formulations of classification decision tree learning algorithm based on induction. We describe two basic parallel formulations. One is based on Synchronous Tree Construction Approach and the other is based on Partitioned Tree Construction Approach. We discuss the advantages and disadvantages of using these methods and propose a hybrid method that employs the good features of these methods. We also provide the analysis of the cost of computation and communication of the proposed hybrid method. Moreover, experimental results on an IBM SP-2 demonstrate excellent speedups and scalability.     

Fisher’s decision tree

Univariate decision trees are classifiers currently used in many data mining applications. This classifier discovers partitions in the input space via hyperplanes that are orthogonal to the axes of attributes, producing a model that can be understood by human experts. One disadvantage of univariate decision trees is that they produce complex and inaccurate models when decision boundaries are not orthogonal to axes. In this paper we introduce the Fisher’s Tree, it is a classifier that takes advantage of dimensionality reduction of Fisher’s linear discriminant and uses the decomposition strategy of decision trees, to come up with an oblique decision tree. Our proposal generates an artificial attribute that is used to split the data in a recursive way.The Fisher’s decision tree induces oblique trees whose accuracy, size, number of leaves and training time are competitive with respect to other decision trees reported in the literature. We use more than ten public available data sets to demonstrate the effectiveness of our method.     

OSAF-tree--可迭代的移动序列模式挖掘及增量更新方法

移动通信技术和无限定位技术的发展积累了海量的、动态增长的时空数据．利用数据挖掘技术从移动用户的时空行为轨迹当中挖掘用户移动序列模式,在移动通信、交通管理、基于位置服务等领域有着广泛的应用前景．由于移动环境网络资源珍贵、数据量大的特点,传统的序列模式挖掘方法在效率上很难满足需求．OSAF-tree算法基于投影的概念,只需要对数据库进行一遍扫描,就可以很好地处理移动序列模式的挖掘及其增量更新和迭代挖掘问题,这是一个非常高效的算法．与已有的方法相比,OSAF-tree算法在性能和I/O代价等方面都具有明显的优势．     

Diversification for better classification trees

Classification trees are widely used in the data mining community. Typically, trees are constructed to try and maximize their mean classification accuracy. In this paper, we propose an alternative to using the mean accuracy as the performance measure of a tree. We investigate the use of various percentiles (representing the risk aversion of a decision maker) of the distribution of classification accuracy in place of the mean. We develop a genetic algorithm (GA) to build decision trees based on this new criterion. We develop this GA further by explicitly creating diversity in the population by simultaneously considering two fitness criteria within the GA. We show that our bicriterion GA performs quite well, scales up to handle large data sets, and requires a small sample of the original data to build a good decision tree.     

Optimal constraint-based decision tree induction from itemset lattices

In this article we show that there is a strong connection between decision tree learning and local pattern mining. This connection allows us to solve the computationally hard problem of finding optimal decision trees in a wide range of applications by post-processing a set of patterns: we use local patterns to construct a global model. We exploit the connection between constraints in pattern mining and constraints in decision tree induction to develop a framework for categorizing decision tree mining constraints. This framework allows us to determine which model constraints can be pushed deeply into the pattern mining process, and allows us to improve the state-of-the-art of optimal decision tree induction.     

Stair Climber Smart Mobile Robot (MSRox)

Most current mobile robots are designed to determine their actions according to their positions. Before making a decision, they need to localize themselves. Thus, their observation strategies are mainly for self-localization. However, observation strategies should not only be for self-localization but also for decision making. We propose an observation strategy that enables a mobile robot to make a decision. It enables a robot equipped with a limited viewing angle camera to make decisions without self-localization. A robot can make a decision based on a decision tree and on prediction trees of observations constructed from its experiences. The trees are constructed based on an information criterion for the action decision, not for self-localization or state estimation. The experimental results with a four legged robot are shown and discussed.     

数据挖掘中决策树算法的最新进展

概述了传统决策树方法的基本原理和优越性,指出了该方法应用于超大数据集的数据挖掘环境时的局限性;着重分五个方面概括了近年来决策树方法在数据挖掘中的主要进展,并讨论了决策树方法面临的挑战及其发展趋势。     

Inducing decision trees with an ant colony optimization algorithm

Decision trees have been widely used in data mining and machine learning as a comprehensible knowledge representation. While ant colony optimization (ACO) algorithms have been successfully applied to extract classification rules, decision tree induction with ACO algorithms remains an almost unexplored research area. In this paper we propose a novel ACO algorithm to induce decision trees, combining commonly used strategies from both traditional decision tree induction algorithms and ACO. The proposed algorithm is compared against three decision tree induction algorithms, namely C4.5, CART and cACDT, in 22 publicly available data sets. The results show that the predictive accuracy of the proposed algorithm is statistically significantly higher than the accuracy of both C4.5 and CART, which are well-known conventional algorithms for decision tree induction, and the accuracy of the ACO-based cACDT decision tree algorithm.     

用粗糙集理论建立决策树的一种方法

在数据挖掘中，分期是一个很重要的问题，有很多流行的分类器可以创建决策树木产生类模型。本文介绍了通过信息增益或熵的比较来构造一棵决策树的数桩挖掘算法思想，给出了用粗糙集理论构造决策树的一种方法，并用曲面造型方面的实例说明了决策树的生成过程。通过与ID3方法的比较，该种方法可以降低决策树的复杂性，优化决策树的结构，能挖掘较好的规则信息。     

Efficiently mining frequent trees in a forest: algorithms and applications

Mining frequent trees is very useful in domains like bioinformatics, Web mining, mining semistructured data, etc. We formulate the problem of mining (embedded) subtrees in a forest of rooted, labeled, and ordered trees. We present TREEMINER, a novel algorithm to discover all frequent subtrees in a forest, using a new data structure called scope-list. We contrast TREEMINER with a pattern matching tree mining algorithm (PATTERNMATCHER), and we also compare it with TREEMINERD, which counts only distinct occurrences of a pattern. We conduct detailed experiments to test the performance and scalability of these methods. We also use tree mining to analyze RNA structure and phylogenetics data sets from bioinformatics domain.     

基于粗集和距离函数的决策树构造方法

决策树是一种有效用于分类的数据挖掘方法.在决策树构造算法中,粗集理论的相对核已被应用于解决多变量检验中属性的选择问题.考虑到决策树技术和粗集的优缺点,将二者结合起来,先对每个结点包含的属性个数加以限制,再用属性相关度和De Mantaras距离函数选择相关的属性组合作为属性选择的标准,给出一种新的构造算法.该算法的优点是能有效降低树的高度,而且增强了分类规则的可读性.     

多决策树的模糊积分融合在银行信贷管理系统中的应用

为了提高基于数据挖掘的商业银行信贷管理系统的信贷风险评估水平,将多决策树的Choquet模糊积分融合(MTCFF)模型应用到银行信贷管理系统中。基本思想是采用决策树在已知类型的客户数据上进行挖掘,按照决策树剪枝程度不同形成不同的决策树并产生规则,利用所生成的不同决策树的规则,对未知类型的客户数据进行分类,然后让Choquet模糊积分对多棵决策树的分类结果进行融合,形成最优判断。采用UCI数据库中German客户信用卡数据集进行验证,实验证明Choquet模糊积分的非线性融合效果优于单棵决策树的分类效果,也优于其他线性融合方法,并且Choquet模糊积分要优于Sugeno模糊积分。     

论促使决策树重构的因素

本文首先阐述了数据挖掘中决策树的基本思想,然后简单介绍了决策树经典算法（ID3算法）,重点基于ID3算法论述了对于决策树的影响4个要素,并使用真实的数据详细地分析了4个要素,实验表明,只要4个要素中的任何一个改变,决策树必须要重新被构建。     

Geometric decision tree

In this paper, we present a new algorithm for learning oblique decision trees. Most of the current decision tree algorithms rely on impurity measures to assess the goodness of hyperplanes at each node while learning a decision tree in top-down fashion. These impurity measures do not properly capture the geometric structures in the data. Motivated by this, our algorithm uses a strategy for assessing the hyperplanes in such a way that the geometric structure in the data is taken into account. At each node of the decision tree, we find the clustering hyperplanes for both the classes and use their angle bisectors as the split rule at that node. We show through empirical studies that this idea leads to small decision trees and better performance. We also present some analysis to show that the angle bisectors of clustering hyperplanes that we use as the split rules at each node are solutions of an interesting optimization problem and hence argue that this is a principled method of learning a decision tree.     

决策树算法及其常见问题的解决

决策树这种数据挖掘技术是目前最有影响和使用最多的数据挖掘技术之一,生成决策树的算法也比较多,但是在这些生成决策树的算法中都需要解决两个问题——数据过分近似和测试属性的选择。     

Tree pattern mining with tree automata constraints

Most work on pattern mining focuses on simple data structures such as itemsets and sequences of itemsets. However, a lot of recent applications dealing with complex data like chemical compounds, protein structures, XML and Web log databases and social networks, require much more sophisticated data structures such as trees and graphs. In these contexts, interesting patterns involve not only frequent object values (labels) appearing in the graphs (or trees) but also frequent specific topologies found in these structures. Recently, several techniques for tree and graph mining have been proposed in the literature. In this paper, we focus on constraint-based tree pattern mining. We propose to use tree automata as a mechanism to specify user constraints over tree patterns. We present the algorithm CoBMiner which allows user constraints specified by a tree automata to be incorporated in the mining process. An extensive set of experiments executed over synthetic and real data (XML documents and Web usage logs) allows us to conclude that incorporating constraints during the mining process is far more effective than filtering the interesting patterns after the mining process.