Data Mining - Concepts and Techniques.

25. Multiple Imputation for Nonresponse in Surveys. By D. B. Rubin. ISBN 0 471 08705 X. Wiley, Chichester, 1987. 258 pp. £30.25.

Multiple Imputation for Nonresponse in Surveys

Accurate and timely traffic flow information is important for the successful deployment of intelligent transportation systems. Over the last few years, traffic data have been exploding, and we have truly entered the era of big data for transportation. Existing traffic flow prediction methods mainly use shallow traffic prediction models and are still unsatisfying for many real-world applications. This situation inspires us to rethink the traffic flow prediction problem based on deep architecture models with big traffic data. In this paper, a novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently. A stacked autoencoder model is used to learn generic traffic flow features, and it is trained in a greedy layerwise fashion. To the best of our knowledge, this is the first time that a deep architecture model is applied using autoencoders as building blocks to represent traffic flow features for prediction. Moreover, experiments demonstrate that the proposed method for traffic flow prediction has superior performance.

Traffic Flow Prediction With Big Data: A Deep Learning Approach

http://bcpw.bg.pw.edu.pl/Content/1968/Rudiments.pdf

Rudiments of rough sets

The Analysis of Time Series: An Introduction, 4th edn. By C. Chatfield. ISBN 0 412 31820 2. Chapman and Hall, London, 1989. 242 pp. £13.50.

The Analysis of Time Series: An Introduction

Data collection and analysis in web mining faces certain unique challenges. Due to a variety of reasons inherent in web browsing and web logging, the likelihood of bad or incomplete data is higher than conventional applications. The analytical techniques in web mining need to accommodate such data. Fuzzy and rough sets provide the ability to deal with incomplete and approximate information. Fuzzy set theory has been shown to be useful in three important aspects of web and data mining, namely clustering, association, and sequential analysis. There is increasing interest in research on clustering based on rough set theory. Clustering is an important part of web mining that involves finding natural groupings of web resources or web users. Researchers have pointed out some important differences between clustering in conventional applications and clustering in web mining. For example, the clusters and associations in web mining do not necessarily have crisp boundaries. As a result, researchers have studied the possibility of using fuzzy sets in web mining clustering applications. Recent attempts have used genetic algorithms based on rough set theory for clustering. However, the genetic algorithms based clustering may not be able to handle the large amount of data typical in a web mining application. This paper proposes a variation of the K-means clustering algorithm based on properties of rough sets. The proposed algorithm represents clusters as interval or rough sets. The paper also describes the design of an experiment including data collection and the clustering process. The experiment is used to create interval set representations of clusters of web visitors.

Interval Set Clustering of Web Users with Rough K -Means

Analyses from some of the highway agencies show that up to 50% permanent traffic counts (PTCs) have missing values It will be difficult to eliminate such a significant portion of data from traffic analysis Literature review indicates that the limited research uses factor or autoregressive integrated moving average (ARIMA) models for predicting missing values Factor-based models tend to be less accurate ARIMA models only use the historical data In this study, genetically designed neural network and regression models, factor models, and ARIMA models were developed It was found that genetically designed regression models based on data from before and after the failure had the most accurate results Average errors for refined models were lower than 1% and the 95th percentile errors were below 2% for counts with stable patterns Even for counts with relatively unstable patterns, average errors were lower than 3% in most cases

Estimation of missing traffic counts using factor, genetic, neural and regression techniques

Soft clustering -- Fuzzy and rough approaches and their extensions and derivatives

This paper reviews and examines interpretations of belief functions in the theory of rough sets with finite universe. The concept of standard rough set algebras is generalized in two directions. One is based on the use of nonequivalence relations. The other is based on relations over two universes, which leads to the notion of interval algebras. Pawlak rough set algebras may be used to interpret belief functions whose focal elements form a partition of the universe. Generalized rough set algebras using nonequivalence relations may be used to interpret belief functions which have less than | U | focal elements, where | U | is the cardinality of the universe U on which belief functions are defined. Interval algebras may be used to interpret any belief functions.

/pdf/interpretations-of-belief-functions-in-the-theory-of-rough-3tja75sxi2.pdf

Interpretations of belief functions in the theory of rough sets

This article examines basic issues of data mining using the theory of rough sets, which is a recent proposal for generalizing classical set theory. The Pawlak rough set model is based on the concept of an equivalence relation. Recent research has shown that a generalized rough set model need not be based on equivalence relation axioms. The Pawlak rough set model has been used for deriving deterministic as well as probabilistic rules from a complete database. This article demonstrates that a generalized rough set model can be used for generating rules from incomplete databases. These rules are based on plausibility functions proposed by Shafer. The article also discusses the importance of rule extraction from incomplete databases in data mining. © 1998 John Wiley & Sons, Inc.

Pawan Lingras

Papers

Interval Set Clustering of Web Users with Rough K -Means

Estimation of missing traffic counts using factor, genetic, neural and regression techniques

Soft clustering -- Fuzzy and rough approaches and their extensions and derivatives

Interpretations of belief functions in the theory of rough sets

Data mining using extensions of the rough set model