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Importance of Dimensionality Reduction in Data mining

The recent explosion of data set size, in number of records as well as of attributes, has triggered the development of a number of big data platforms as well as parallel data analytics algorithms. At the same time though, it has pushed for the usage of data dimensionality reduction procedures. Dealing with a lot of dimensions can be painful for machine learning algorithms. High dimensionality will increase the computational complexity, increase the risk of over fitting (as your algorithm has more degrees of freedom) and the sparsity of the data will grow. Hence, dimensionality reduction will project the data in a space with fewer dimensions to limit these phenomena. What is Dimensionality Reduction? The problem of unwanted increase in dimension is closely related to fixation of measuring / recording data at a far granular level then it was done in past. This is no way suggesting that this is a recent problem. It has started gaining more importance lately due to surge in data. In machine learning classification problems, there are often too many factors on the basis of which the final classification is done. These factors are basically variables called features. The higher the number of features, the harder…

Rule based System

Knowledge is practical or theoretical understanding of a subject or domain. Thus who possess knowledge are called experts. The human mental process is internal, it is too complex to be represented as an algorithm. However, most experts are capable of expressing their knowledge in the form of rules for problem solving. Rules are the popular paradigm for representing knowledge. A rule based expert system is one whose knowledge base contains the domain knowledge coded in the form of rules. Overview of Rule Based Systems Instead of representing knowledge in a relatively declarative, static way (as a bunch of things that are true), rule based system represent knowledge in terms of a bunch of rules that tell you what you should do or what you could conclude in different situations. A rule-based system consists of a bunch of IF-THEN rules, a bunch of facts, and some interpreter controlling the application of the rules, given the facts Rule-based systems (also known as production systems or expert systems) are the simplest form of artificial intelligence. A rule based system uses rules as the knowledge representation for knowledge coded into the system. The definitions of rule-based system depend almost entirely on expert systems, which…

What is Pattern Recognition

One of the most important capabilities of mankind is learning by experience, by our endeavors, by our faults. By the time we attain an age of five most of us are able to recognize digits, characters; whether it is big or small, uppercase or lowercase, rotated, tilted. We will be able to recognize, even if the character is on a mutilated paper, partially occluded or even on the clustered background. Looking at the history of the human search for knowledge, it is clear that humans are fascinated with recognizing patterns in nature, understand it, and attempt to relate patterns into a set of rules. Informally, a pattern is defined by the common denominator among the multiple instances of an entity. Therefore, Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. Pattern recognition is concerned with the design and development of systems that recognize patterns in data. The purpose of a pattern recognition program is to analyze a scene in the real world and to arrive at a description of the scene which is useful for the accomplishment of some task. Introduction of Pattern Recognition Pattern Recognition is a mature but exciting and fast developing field,…

What is Fingerprint Recognition

Fingerprint Recognition is one of the most well-known and publicized biometrics. Because of their uniqueness and consistency over time, fingerprints have been used for identification for over a century, more recently becoming automated (i.e. a biometric) due to advancements in computing capabilities. Fingerprint identification is popular because of the inherent ease in acquisition, the numerous sources (ten fingers) available for collection, and their established use and collections by law enforcement and immigration. Introduction of Fingerprint Recognition Fingerprint recognition is one of most popular and accuracy Biometric technologies. Fingerprint recognition (identification) is one of the oldest methods of identification with biometric traits. Large no. of archeological artifacts and historical items shows the signs of fingerprints of human on stones. The ancient people were aware about the individuality of fingerprint, but they were not aware of scientific methods of finding individuality. Fingerprints have remarkable permanency and uniqueness throughout the time. Fingerprints offer more secure and reliable personal identification than passwords, id-cards or key can provide. Examples such as computers and mobile phones equipped with fingerprint sensing devices for fingerprint based password protection are being implemented to replace ordinary password protection methods. Finger-scan technology is the most widely deployed biometric technology, with a…

Information Retrieval System and Applications

Information retrieval (IR) is the field of computer science that deals with the processing of documents containing free text, so that they can be rapidly retrieved based on keywords specified in a user’s query. The effectiveness of IR systems is measured by comparing performance on a common set of queries and documents. The meaning of the term IR can be very broad. Just getting a credit card out of your wallet so that you can type in the card number is a form of IR. However, as an academic field of study, information retrieval might be defined thus: What is information retrieval ? Information retrieval is generally considered as a subfield of computer science that deals with the representation, storage, and access of information. Information retrieval is concerned with the organization and retrieval of information from large database collections Information Retrieval (IR) is the science of searching for information within relational databases, documents, text, multimedia files, and the World Wide Web. Information retrieval is accomplished by means of an information retrieval system and is performed manually or with the use of mechanization or automation. Human beings are indispensable in information retrieval. Depending on the character of the information contained in the…

Introduction of Brain Computer Interface

As the power of modern computers grows alongside our understanding of the human brain, we move ever closer to making some pretty spectacular science fiction into reality. Imagine transmitting signals directly to someone’s brain that would allow them to see, hear or feel specific sensory inputs. Consider the potential to manipulate computers or machinery with nothing more than a thought. It isn’t about convenience — for severely disabled people, development of a brain-computer interface (BCI) could be the most important technological breakthrough in decades. In this article, we’ll learn all about how BCIs work, their limitations and where they could be headed in the future. what is Brain Computer Interface Brain computer interface technology represents a highly growing field of research with application systems. Its contributions in medical fields range from prevention to neuronal rehabilitation for serious injuries. Brain Computer Interface (BCI) technology is a powerful communication tool between users and systems. It does not require any external devices or muscle intervention to issue commands and complete the interaction Definition of Brain Computer Interface A BCI is a computer-based system that acquires brain signals, analyzes them, and translates them into commands that are relayed to an output device to carry out a…

Introduction of HopField Neural Network

Human beings are constantly thinking since ages about the reasons for human capabilities and incapabilities. Successful attempts have been made to design and develop systems that emulate human capabilities or help overcome human incapabilities. The human brain, which has taken millions of years to evolve to its present architecture, excels at tasks such as vision, speech, information retrieval, complex pattern recognition, all of which are extremely difficult tasks for conventional computers. A number of mechanisms have been which seems to enable human brain to handle various problems. These mechanisms include association; generalization and self-organization. A brain similar computational technique namely HopField Neural Network is explained here. Working of Hop Field Neural Network A neural network (or more formally artificial neural network) is a mathematical model or computational model inspired by the structure and functional aspects of biological neural networks. It consists of an interconnected group of artificial neurons. The original inspiration for the term Artificial Neural Network came from examination of central nervous systems and their neurons, axons, dendrites and synapses which constitute the processing elements of biological neural networks. One of the milestones for the current renaissance in the field of neural networks was the associative model proposed by Hopfield at…

Introduction of Image Classification

Classification includes a broad range of decision-theoretic approaches to the identification of images (or parts thereof). All classification algorithms are based on the assumption that the image in question depicts one or more features (e.g., geometric parts in the case of a manufacturing classification system, or spectral regions in the case of remote sensing, as shown in the examples below) and that each of these features belongs to one of several distinct and exclusive classes. The classes may be specified a priori by an analyst (as in supervised classification) or automatically clustered (i.e. as in unsupervised classification) into sets of prototype classes, where the analyst merely specifies the number of desired categories. (Classification and segmentation (clustering) have closely related objectives, as the former is another form of component labeling that can result in segmentation of various features in a scene.) Definition of Image Classification Image classification is the process of assigning land cover classes to pixels. Image classification refers to the task of extracting information classes from a multiband raster image. The resulting raster from image classification can be used to create thematic maps. Depending on the interaction between the analyst and the computer during classification, there are two types of classification:…

Introduction of Automatic Speech Recognition

Speech is a versatile mean of communication. It conveys linguistic speaker and environmental information. Even though such information is encoded in a complex form, humans can relatively decode most of it. Among all speech tasks, automatic speech recognition (ASR) has been the focus of many researchers for several decades. In this task, the linguistic message is the information of interest. Speech recognition applications range from dictating a text to generating subtitles in real-time for a television broadcast. Despite the human ability, researchers learned that extracting information from speech is not a straightforward process. Definition of Automatic Speech Recognition Speech recognition has in years has become a practical concept, which is now being implemented in different languages around the world. Speech recognition has been used in real-world human language applications, such as information recovery. Speech in human can be said as the most common means of the communication because the information maintains the basic role in conversation. The conversation or speech that is captured by a microphone or a telephone is converted from acoustic signal to a set of words in speech recognition. It can be defined: “Automatic speech recognition (ASR) can be defined as the independent, computer‐driven transcription of spoken language…

Data Preprocessing in Data Mining

Data analysis is now integral to our working lives. It is the basis for investigations in many fields of knowledge, from science to engineering and from management to process control. Data on a particular topic are acquired in the form of symbolic and numeric attributes. Analysis of these data gives a better understanding of the phenomenon of interest. When development of a knowledge-based system is planned, the data analysis involves discovery and generation of new knowledge for building a reliable and comprehensive knowledge base. What is Data Preprocessing Exploratory data analysis and predictive analytics can be used to extract hidden patterns from data and are becoming increasingly important tools to transform data into information. Real-world data is generally incomplete and noisy, and is likely to contain irrelevant and redundant information or errors. Data preprocessing, which is an important step in data mining processes, helps transform the raw data to an understandable format. Data pre-processing is an important step in the data mining process. It describes any type of processing performed on raw data to prepare it for another processing procedure. Data preprocessing transforms the data into a format that will be more easily and effectively processed for the purpose of the…