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

Introduction to a Transient World. Fourier Kingdom. Discrete Revolution. Time Meets Frequency. Frames. Wavelet Zoom. Wavelet Bases. Wavelet Packet and Local Cosine Bases. An Approximation Tour. Estimations are Approximations. Transform Coding. Appendix A: Mathematical Complements. Appendix B: Software Toolboxes.

http://ahvazdownload.persiangig.com/document/article/39.pdf

A wavelet tour of signal processing

In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinations of these atoms. Applications that use sparse representation are many and include compression, regularization in inverse problems, feature extraction, and more. Recent activity in this field has concentrated mainly on the study of pursuit algorithms that decompose signals with respect to a given dictionary. Designing dictionaries to better fit the above model can be done by either selecting one from a prespecified set of linear transforms or adapting the dictionary to a set of training signals. Both of these techniques have been considered, but this topic is largely still open. In this paper we propose a novel algorithm for adapting dictionaries in order to achieve sparse signal representations. Given a set of training signals, we seek the dictionary that leads to the best representation for each member in this set, under strict sparsity constraints. We present a new method-the K-SVD algorithm-generalizing the K-means clustering process. K-SVD is an iterative method that alternates between sparse coding of the examples based on the current dictionary and a process of updating the dictionary atoms to better fit the data. The update of the dictionary columns is combined with an update of the sparse representations, thereby accelerating convergence. The K-SVD algorithm is flexible and can work with any pursuit method (e.g., basis pursuit, FOCUSS, or matching pursuit). We analyze this algorithm and demonstrate its results both on synthetic tests and in applications on real image data

https://users.ece.utexas.edu/~sanghavi/courses/papers/KSVD.pdf

$rm K$ -SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation

An efficient and intuitive algorithm is presented for the design of vector quantizers based either on a known probabilistic model or on a long training sequence of data. The basic properties of the algorithm are discussed and demonstrated by examples. Quite general distortion measures and long blocklengths are allowed, as exemplified by the design of parameter vector quantizers of ten-dimensional vectors arising in Linear Predictive Coded (LPC) speech compression with a complicated distortion measure arising in LPC analysis that does not depend only on the error vector.

/pdf/an-algorithm-for-vector-quantizer-design-266rr8cpmt.pdf

An Algorithm for Vector Quantizer Design

We propose a novel image denoising strategy based on an enhanced sparse representation in transform domain. The enhancement of the sparsity is achieved by grouping similar 2D image fragments (e.g., blocks) into 3D data arrays which we call "groups." Collaborative Altering is a special procedure developed to deal with these 3D groups. We realize it using the three successive steps: 3D transformation of a group, shrinkage of the transform spectrum, and inverse 3D transformation. The result is a 3D estimate that consists of the jointly filtered grouped image blocks. By attenuating the noise, the collaborative filtering reveals even the finest details shared by grouped blocks and, at the same time, it preserves the essential unique features of each individual block. The filtered blocks are then returned to their original positions. Because these blocks are overlapping, for each pixel, we obtain many different estimates which need to be combined. Aggregation is a particular averaging procedure which is exploited to take advantage of this redundancy. A significant improvement is obtained by a specially developed collaborative Wiener filtering. An algorithm based on this novel denoising strategy and its efficient implementation are presented in full detail; an extension to color-image denoising is also developed. The experimental results demonstrate that this computationally scalable algorithm achieves state-of-the-art denoising performance in terms of both peak signal-to-noise ratio and subjective visual quality.

Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering

This paper presents an LSP quantization design method for bandwidth scalable coders such as the MPEG-4 CELP coder. In the enhancement layer of these coders, the LSP parameters are quantized using both interframe and intraframe predictors. The proposed design algorithm enables us to jointly optimize these predictors. Objective and subjective test results show that the quantizer obtained with the proposed algorithm provides better performance than that used in the MPEG-4 CELP.

/pdf/enhancing-mpeg-4-celp-by-jointly-optimized-inter-intra-frame-4bj50zm2bn.pdf

Enhancing MPEG-4 CELP by jointly optimized inter/intra-frame LSP predictors

We present a novel scheme for hybrid coding of speech signals. This hybrid codec utilizes the excitation/filter model used extensively for speech coding. Similar to other modern vocoders, voiced speech is represented by a frequency domain harmonic model and unvoiced speech by a "noise-like" excitation. However, an analysis-by-synthesis time domain scheme is employed for the transitory portions of the speech signal which cannot be adequately represented by either model. Switching between the time domain and the frequency domain models requires careful handling of the reconstructed linear phase. The structure of a 4 kbps speech codec, based on the hybrid model, is outlined. The new codec shows promise of achieving toll quality at 4 kbps.

Hybrid coding of speech at 4 kbps

A study of the combination of VSC (vector excitation coding) with subband coding is reported. In subband VXC (SBVXC), the burden of redundancy removal is split between the frequency domain and time domain to take advantage of both predictive coding and subband coding techniques. The conventional adaptive bit-allocation method used in subband coding has been generalized for use in SBVXC. Simulated comparisons of several coding schemes, including one comparing SBVXC with the full-band VXC, are presented. Results show that a two-band version of SBVXC can produce fairly clear, intelligible, and natural-sounding synthetic speech at 4.8 kb/s and very high quality speech at 8 kb/s with a reasonable complexity. >

Subband vector excitation coding with adaptive bit-allocation

A novel least-squares formulation of the vector linear prediction (VLP) problem is presented. Based on this formulation, we develop two new design methods for obtaining the optimal vector predictor for frame-adaptive prediction: the covariance method and the autocorrelation method, which bear the names of the corresponding methods in scalar LPC analysis. Our formulation reveals several previously unrecognized properties of the resulting normal equation. Simulation results for VLP of speech waveforms confirm that the two proposed methods indeed give higher prediction gain than previously developed methods.

Covariance and autocorrelation methods for vector linear prediction

Vector quantization (VQ) techniques have so far had limited application in high-fidelity or high resolution applications due to computational and storage complexity. While multistage VQ (MSVQ) offers very low storage complexity, it has a much higher distortion than tree-structured VQ. We extend multistage VQ (MSVQ) to a more general product code structure that allows a gradual trade-off between distortion and storage while maintaining a fixed computational complexity. In this approach, each stage of encoding selects one of a set of codebooks for that stage according to the outcome of the prior stage. A limited number of codebooks for each stage are optimally designed by a constrained-storage VQ technique and are optimally shared by a large number of statistically distinct residual vectors.

Allen Gersho

Papers

Enhancing MPEG-4 CELP by jointly optimized inter/intra-frame LSP predictors

Hybrid coding of speech at 4 kbps

Subband vector excitation coding with adaptive bit-allocation

Covariance and autocorrelation methods for vector linear prediction

Enhanced Multistage Vector Quantization with Constrained Storage