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

Statistics for Spatial Data.

The book Knowledge Discovery in Databases, edited by Piatetsky-Shapiro and Frawley [PSF91], is an early collection of research papers on knowledge discovery from data. The book Advances in Knowledge Discovery and Data Mining, edited by Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy [FPSSe96], is a collection of later research results on knowledge discovery and data mining. There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99], Building Data Mining Applications for CRM by Berson, Smith, and Thearling [BST99], Data Mining: Practical Machine Learning Tools and Techniques by Witten and Frank [WF05], Principles of Data Mining (Adaptive Computation and Machine Learning) by Hand, Mannila, and Smyth [HMS01], The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman [HTF01], Data Mining: Introductory and Advanced Topics by Dunham, and Data Mining: Multimedia, Soft Computing, and Bioinformatics by Mitra and Acharya [MA03]. There are also books containing collections of papers on particular aspects of knowledge discovery, such as Machine Learning and Data Mining: Methods and Applications edited by Michalski, Brakto, and Kubat [MBK98], and Relational Data Mining edited by Dzeroski and Lavrac [De01], as well as many tutorial notes on data mining in major database, data mining and machine learning conferences.

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Data Mining: Concepts and Techniques (2nd edition)

Since the time of the industrial revolution, the atmospheric CO(2) concentration has risen by nearly 35 % to its current level of 383 ppm. The increased carbon dioxide concentration in the atmosphere has been suggested to be a leading contributor to global climate change. To slow the increase, reductions in anthropogenic CO(2) emissions are necessary. Large emission point sources, such as fossil-fuel-based power generation facilities, are the first targets for these reductions. A benchmark, mature technology for the separation of dilute CO(2) from gas streams is via absorption with aqueous amines. However, the use of solid adsorbents is now being widely considered as an alternative, potentially less-energy-intensive separation technology. This Review describes the CO(2) adsorption behavior of several different classes of solid carbon dioxide adsorbents, including zeolites, activated carbons, calcium oxides, hydrotalcites, organic-inorganic hybrids, and metal-organic frameworks. These adsorbents are evaluated in terms of their equilibrium CO(2) capacities as well as other important parameters such as adsorption-desorption kinetics, operating windows, stability, and regenerability. The scope of currently available CO(2) adsorbents and their critical properties that will ultimately affect their incorporation into large-scale separation processes is presented.

Adsorbent Materials for Carbon Dioxide Capture from Large Anthropogenic Point Sources

The problem of answering queries using views is to find efficient methods of answering a query using a set of previously defined materialized views over the database, rather than accessing the database relations. The problem has recently received significant attention because of its relevance to a wide variety of data management problems. In query optimization, finding a rewriting of a query using a set of materialized views can yield a more efficient query execution plan. To support the separation of the logical and physical views of data, a storage schema can be described using views over the logical schema. As a result, finding a query execution plan that accesses the storage amounts to solving the problem of answering queries using views. Finally, the problem arises in data integration systems, where data sources can be described as precomputed views over a mediated schema. This article surveys the state of the art on the problem of answering queries using views, and synthesizes the disparate works into a coherent framework. We describe the different applications of the problem, the algorithms proposed to solve it and the relevant theoretical results.

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Answering queries using views: A survey

A data warehouse stores materialized views of data from one or more sources, with the purpose of efficiently implementing decision-support or OLAP queries. One of the most important decisions in designing a data warehouse is the selection of materialized views to be maintained at the warehouse. The goal is to select an appropriate set of views that minimizes total query response time and the cost of maintaining the selected views, given a limited amount of resource, e.g., materialization time, storage space etc.

Selection of Views to Materialize in a Data Warehouse

On-line analytical processing (OLAP) is a recent and important application of database systems. Typically, OLAP data is presented as a multidimensional "data cube." OLAP queries are complex and can take many hours or even days to run, if executed directly on the raw data. The most common method of reducing execution time is to precompute some of the queries into summary tables (subcubes of the data cube) and then to build indexes on these summary tables. In most commercial OLAP systems today, the summary tables that are to be precomputed are picked first, followed by the selection of the appropriate indexes on them. A trial-and-error approach is used to divide the space available between the summary tables and the indexes. This two-step process can perform very poorly. Since both summary tables and indexes consume the same resource-space-their selection should be done together for the most efficient use of space. The authors give algorithms that automate the selection of summary tables and indexes. In particular, they present a family of algorithms of increasing time complexities, and prove strong performance bounds for them. The algorithms with higher complexities have better performance bounds. However, the increase in the performance bound is diminishing, and they show that an algorithm of moderate complexity can perform fairly close to the optimal.

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Index selection for OLAP

This study is focused on improving the reactivity of a CaO sorbent for its use in a reaction-based process for the separation of carbon dioxide (CO2) from flue gas. The separation process consists of cyclical carbonation (of a metal oxide) and calcination (of the metal carbonate formed) reactions to yield concentrated CO2 from flue gas. CaO sorbents synthesized from naturally occurring limestone and dolomite were microporous in nature. Pore filling and pore pluggage of these micropores limited the conversion of CaO in the carbonation reaction to about 45−50% of the stoichiometric limit. A wet precipitation process was tailored to synthesize high-surface-area precipitated calcium carbonate (PCC). The pores of PCC predominantly lie in the mesoporous range (5−20 nm). The CaO sorbent obtained from PCC (PCC-CaO) was less susceptible to pore pluggage and attained over 90% conversion. PCC-CaO was also capable of maintaining its high reactivity (>90%) over two carbonation−calcination cycles.

Carbonation−Calcination Cycle Using High Reactivity Calcium Oxide for Carbon Dioxide Separation from Flue Gas

Spatial query execution is an essential functionality of a sensor network, where a query gathers sensor data within a specific geographic region. Redundancy within a sensor network can be exploited to reduce the communication cost incurred in execution of such queries. Any reduction in communication cost would result in an efficient use of the battery energy, which is very limited in sensors. One approach to reduce the communication cost of a query is to self-organize the network, in response to a query, into a topology that involves only a small subset of the sensors sufficient to process the query. The query is then executed using only the sensors in the constructed topology. The self-organization technique is beneficial for queries that run sufficiently long to amortize the communication cost incurred in self-organization. In this paper, we design and analyze algorithms for suchself-organization of a sensor network to reduce energy consumption. In particular, we develop the notion of a connected sensor cover and design a centralized approximation algorithm that constructs a topology involving a near-optimal connected sensor cover. We prove that the size of the constructed topology is within an O(logn) factor of the optimal size, where n is the network size. We develop a distributed self-organization version of the approximation algorithm, and propose several optimizations to reduce the communication overhead of the algorithm. We also design another distributed algorithm based on node priorities that has a further lower communication overhead, but does not provide any guarantee on the size of the connected sensor cover constructed. Finally, we evaluate the distributed algorithms using simulations and show that our approaches results in significant communication cost reductions.

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Connected sensor cover: self-organization of sensor networks for efficient query execution

In this paper, we consider multihop wireless mesh networks, where each router node is equipped with multiple radio interfaces, and multiple channels are available for communication. We address the problem of assigning channels to communication links in the network with the objective of minimizing the overall network interference. Since the number of radios on any node can be less than the number of available channels, the channel assignment must obey the constraint that the number of different channels assigned to the links incident on any node is at most the number of radio interfaces on that node. The above optimization problem is known to be NP-hard. We design centralized and distributed algorithms for the above channel assignment problem. To evaluate the quality of the solutions obtained by our algorithms, we develop a semidefinite program and a linear program formulation of our optimization problem to obtain lower bounds on overall network interference. Empirical evaluations on randomly generated network graphs show that our algorithms perform close to the above established lower bounds, with the difference diminishing rapidly with increase in number of radios. Also, ns-2 simulations, as well as experimental studies on testbed, demonstrate the performance potential of our channel assignment algorithms in 802.11-based multiradio mesh networks.

Himanshu Gupta

Papers

Selection of Views to Materialize in a Data Warehouse

Index selection for OLAP

Carbonation−Calcination Cycle Using High Reactivity Calcium Oxide for Carbon Dioxide Separation from Flue Gas

Connected sensor cover: self-organization of sensor networks for efficient query execution

Minimum Interference Channel Assignment in Multiradio Wireless Mesh Networks