The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

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Data Mining: Concepts and Techniques

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.

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Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

Recommender systems apply knowledge discovery techniques to the problem of making personalized recommendations for information, products or services during a live interaction. These systems, especially the k-nearest neighbor collaborative ltering based ones, are achieving widespread success on the Web. The tremendous growth in the amount of available information and the number of visitors to Web sites in recent years poses some key challenges for recommender systems. These are: producing high quality recommendations, performing many recommendations per second for millions of users and items and achieving high coverage in the face of data sparsity. In traditional collaborative ltering systems the amount of work increases with the number of participants in the system. New recommender system technologies are needed that can quickly produce high quality recommendations, even for very large-scale problems. To address these issues we have explored item-based collaborative ltering techniques. Item-based techniques rst analyze the user-item matrix to identify relationships between di erent items, and then use these relationships to indirectly compute recommendations for users. In this paper we analyze di erent item-based recommendation generation algorithms. We look into di erent techniques for computing item-item similarities (e.g., item-item correlation vs. cosine similarities between item vectors) and di erent techniques for obtaining recommendations from them (e.g., weighted sum vs. regression model). Finally, we experimentally evaluate our results and compare them to the basic k-nearest neighbor approach. Our experiments suggest that item-based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.

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Item-based collaborative filtering recommendation algorithms

Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.

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Evaluating collaborative filtering recommender systems

The clustering problem is well known in the database literature for its numerous applications in problems such as customer segmentation, classification and trend analysis. Unfortunately, all known algorithms tend to break down in high dimensional spaces because of the inherent sparsity of the points. In such high dimensional spaces not all dimensions may be relevant to a given cluster. One way of handling this is to pick the closely correlated dimensions and find clusters in the corresponding subspace. Traditional feature selection algorithms attempt to achieve this. The weakness of this approach is that in typical high dimensional data mining applications different sets of points may cluster better for different subsets of dimensions. The number of dimensions in each such cluster-specific subspace may also vary. Hence, it may be impossible to find a single small subset of dimensions for all the clusters. We therefore discuss a generalization of the clustering problem, referred to as the projected clustering problem, in which the subsets of dimensions selected are specific to the clusters themselves. We develop an algorithmic framework for solving the projected clustering problem, and test its performance on synthetic data.

Fast algorithms for projected clustering

This paper introduces a new and novel approach to ratingbased collaborative filtering. The new technique is most appropriate for e-commerce merchants offering one or more groups of relatively homogeneous items such as compact disks, videos, books, software and the like. In contrast with other known collaborative filtering techniques, the new algorithm is graph-theoretic, based on the twin new concepts of ho&rag and predictability. As is demonstrated in this paper, the technique is fast, scalable, accurate, and requires only a modest learning curve. It makes use of a hierarchical classification scheme in order to introduce context into the rating process, and uses so-called creative links in order to find surprising and atypical items to recommend, perhaps even items which cross the group boundaries. The new technique is one of the key engines of the Intelligent Recommendation Algorithm (IRA) project, now being developed at IBM Research. In addition to several other recommendation engines, IRA contains a situation analyzer to determine the most appropriate mix of engines for a particular e-commerce merchant, as well as an engine for optimizing the placement of advertisements.

Horting hatches an egg: a new graph-theoretic approach to collaborative filtering

With the recent explosion in usage of the World Wide Web, the problem of caching Web objects has gained considerable importance. Caching on the Web differs from traditional caching in several ways. The nonhomogeneity of the object sizes is probably the most important such difference. In this paper, we give an overview of caching policies designed specifically for Web objects and provide a new algorithm of our own. This new algorithm can be regarded as a generalization of the standard LRU algorithm. We examine the performance of this and other Web caching algorithms via event- and trace-driven simulation.

Caching on the World Wide Web

In a video-on-demand environment, batching of video requests is often used to reduce I/O demand and improve throughput. Since viewers may defect if they experience long waits, a good video scheduling policy needs to consider not only the batch size but also the viewer defection probabilities and wait times. Two conventional scheduling policies for batching are first-come-first-served (FCFS) and maximum queue length (MOL). Neither of these policies lead to entirely satisfactory results. MQL tends to be too aggressive in scheduling popular videos by only considering the queue length to maximize batch size, while FCFS has the opposite effect. We introduce the notion of factored queue length and propose a batching policy that schedules the video with the maximum factored queue length. We refer to this as the MFQ policy. The factored queue length is obtained by weighting each video queue length with a factor which is biased against the more popular videos. An optimization problem is formulated to solve the best weighting factors for the various videos. A simulation is developed to compare the proposed MFQ policy with FCFS and MQL. Our study shows that MFQ yields excellent empirical results in terms of standard performance measures such as average latency time, defection rates and fairness.

On optimal batching policies for video-on-demand storage servers

As streaming video and audio over the Internet becomes popular, proper proxy caching of large multimedia objects has become increasingly important. For a large media object, such as a 2-hour video, treating the whole video as a single web object for caching is not appropriate. In this paper, we present and evaluate a segment-based bu er management approach to proxy caching of large media streams. Blocks of a media stream received by a proxy server are grouped into variable-sized segments. The cache admission and replacement policies then attach di erent caching values to di erent segments, taking into account the segment distance from the start of the media. These caching policies give preferential treatments to the beginning segments. As such, users can quickly play back the media objects without much delay. Event-driven simulations are conducted to evaluate this segment-based proxy caching approach. The results show that (1) segment-based caching is e ective not only in increasing byte-hit ratio (or reducing total traAEc) but also in lowering the number of requests that require delayed starts; (2) segment-based caching is especially advantageous when the cache size is limited, when the set of hot media objects changes over time, when the media le size is large, and when many users may stop playing the media after only a few initial blocks.

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Joel L. Wolf

Papers

Fast algorithms for projected clustering

Horting hatches an egg: a new graph-theoretic approach to collaborative filtering

Caching on the World Wide Web

On optimal batching policies for video-on-demand storage servers

Segment-based proxy caching of multimedia streams