What is Data Stream in Data Mining

In today’s information society, computer users are used to gathering and sharing data anytime and anywhere. This concerns applications such as social networks, banking, telecommunication, health care, research, and entertainment, among others. As a result, a huge amount of data related to all human activity is gathered for storage and processing purposes. These data sets may contain interesting and useful knowledge represented by hidden patterns, but due to the volume of the gathered data it is impossible to manually extract that knowledge. Data streaming requires some combination of bandwidth sufficiency and, for real-time human perception of the data, the ability to make sure that enough data is being continuously received without any noticeable time lag. What is it? Streaming Data is data that is generated continuously by thousands of data sources, which typically send in the data records simultaneously, and in small sizes (order of Kilobytes). Streaming data includes a wide variety of data such as log files generated by customers using your mobile or web applications, e-commerce purchases, in-game player activity, information from social networks, financial trading floors, or Geo-spatial services, and telemetry from connected devices or instrumentation in data centers. This data needs to be processed sequentially and incrementally…

Multi Agent System in Artificial Intelligence

Multi-agent systems are made up of multiple interacting intelligent agents—computational entities to some degree autonomous and able to cooperate, compete, communicate, act flexibly, and exercise control over their behavior within the frame of their objectives. They are the enabling technology for a wide range of advanced applications relying on distributed and parallel processing of data, information, and knowledge relevant in domains ranging from industrial manufacturing to e-commerce to health care. What is Multi-agent system? In artificial intelligence research, agent-based systems technology has been hailed as a new paradigm for conceptualizing, designing, and implementing software systems. Agents are sophisticated computer programs that act autonomously on behalf of their users, across open and distributed environments, to solve a growing number of complex problems. Increasingly, however, applications require multiple agents that can work together. A multi-agent system (MULTI-AGENT SYSTEM) is a loosely coupled network of software agents that interact to solve problems that are beyond the individual capacities or knowledge of each problem solver. Multi-agent system can be define by the following definition: “A multi-agent system is a loosely coupled network of problem-solving entities (agents) that work together to find answers to problems that are beyond the individual capabilities or knowledge of each…

Introduction of Context Aware Computing

Context-aware computing promises a smooth interaction between humans and technology but few studies have been conducted with regards to how autonomously an application should perform. Context-aware computing is a style of computing in which situational and environmental information about people, places and things is used to anticipate immediate needs and proactively offer enriched, situation-aware and usable content, functions and experiences. The notion of context is much more widely appreciated today. The term “context-aware computing” is commonly understood by those working in ubiquitous/pervasive computing, where it is felt that context is a key in their efforts to disperse and enmesh computation into our lives. Overview of Context-aware computing Context is a powerful, and longstanding, concept in human-computer interaction. Interaction with computation is by explicit acts of communication (e.g., pointing to a menu item), and the context is implicit (e.g., default settings). Context can be used to interpret explicit acts, making communication much more efficient. Thus, by carefully embedding computing into the context of our lived activities, it can serve us with minimal effort on our part. Communication can be not only effortless, but also naturally fit in with our ongoing activities. A great deal of effort has gone into the field of…

How Business Analytics Works

Every business is dynamic in nature and is affected by various external and internal factors. These factors include external market conditions, competitors, internal restructuring and re-alignment, operational optimization and paradigm shifts in the business itself. New regulations and restrictions, in combination with the above factors, contribute to the constant evolutionary nature of compelling, business-critical information; the kind of information that an organization needs to sustain and thrive. Business Intelligence (“BI”) is broad term that encapsulates the process of gathering information pertaining to a business and the market. What is Business Analytics? Business intelligence (BI) has two basic different meanings related to the use of the term intelligence. The primary, less frequently, is the human intelligence capacity applied in business affairs/activities. Intelligence of Business is a new field of the investigation of the application of human cognitive faculties and artificial intelligence technologies to the management and decision support in different business problems. The second relates to the intelligence as information valued for its currency and relevance. It is expert information, knowledge and technologies efficient in the management of organizational and individual business. Therefore, in this sense, business intelligence is a broad category of applications and technologies for gathering, providing access to,…

Introduction of Text Summarization

With the dramatic growth of the Internet, people are overwhelmed by the tremendous amount of online information and documents. This expanding availability of documents has demanded exhaustive research in the area of automatic text summarization. Every day, people rely on a wide variety of sources to stay informed — from news stories to social media posts to search results. Being able to develop Machine Learning models that can automatically deliver accurate summaries of longer text can be useful for digesting such large amounts of information in a compressed form. What is Text Summarization? Text summarization is the problem of creating a short, accurate, and fluent summary of a longer text document. Summarization can also serve as an interesting reading comprehension test for machines. To summarize well, machine learning models need to be able to comprehend documents and distill the important information, tasks which are highly challenging for computers, especially as the length of a document increases. The World Wide Web has brought us a vast amount of on-line information. Due to this fact, every time someone searches something on the Internet, the response obtained is lots of different Web pages with much information, which is impossible for a person to…

What is Sequential Pattern Mining

The rapid growth of the amount of stored digital data and the recent developments in data mining techniques, have lead to an increased interest in methods for the exploration of data, creating a set of new data mining problems and solutions. Frequent Structure Mining is one of these problems. Its target is the discovery of hidden structured patterns in large databases. Sequences are the simplest form of structured patterns. In this article Sequential Pattern Mining is discussed. Introduction of Sequential Pattern Mining Sequential pattern is a set of itemsets structured in sequence database which occurs sequentially with a specific order. A sequence database is a set of ordered elements or events, stored with or without a concrete notion of time. Each itemset contains a set of items which include the same transaction-time value. While association rules indicate intra-transaction relationships, sequential patterns represent the correlation between transactions. Sequential pattern mining discovers which items a single customer, having those items come from various transactions, brings in a particular order. The resulting pattern found after mining is the sequence of item sets that normally found frequent in specific order. Sequential pattern mining is used in various areas for different purposes. It can be used…

Introduction of Natural Language Processing

Natural language processing (NLP) is the relationship between computers and human language. More specifically, natural language processing is the computer understanding, analysis, manipulation, and/or generation of natural language. Will a computer program ever be able to convert a piece of English text into a programmer friendly data structure that describes the meaning of the natural language text? Unfortunately, no consensus has emerged about the form or the existence of such a data structure. Until such fundamental Artificial Intelligence problems are resolved, computer scientists must settle for the reduced objective of extracting simpler representations that describe limited aspects of the textual information. Overview Natural Language processing Natural language processing (NLP) can be defined as the automatic (or semi-automatic) processing of human language. The term ‘NLP’ is sometimes used rather more narrowly than that, often excluding information retrieval and sometimes even excluding machine translation. NLP is sometimes contrasted with ‘computational linguistics’, with NLP being thought of as more applied. Nowadays, alternative terms are often preferred, like ‘Language Technology’ or ‘Language Engineering’. Language is often used in contrast with speech (e.g., Speech and Language Technology). But I’m going to simply refer to NLP and use the term broadly. NLP is essentially multidisciplinary: it is…

Outlier Detection in Data Mining

Outlier detection is a primary step in many data-mining applications. In many data analysis tasks a large number of variables are being recorded or sampled. One of the first steps towards obtaining a coherent analysis is the detection of outlaying observations. Although outliers are often considered as an error or noise, they may carry important information. Detected outliers are candidates for aberrant data that may otherwise adversely lead to model misspecification, biased parameter estimation and incorrect results. It is therefore important to identify them prior to modeling and analysis. Outlier Detection Overview Outlier Detection is an algorithmic feature that allows you to detect when some members of a group are behaving strangely compared to the others. Outlier detection is an important research problem in data mining that aims to find objects that are considerably dissimilar, exceptional and inconsistent with respect to the majority of the data in an input database. Outliers are extreme values that deviate from other observations on data; they may indicate variability in a measurement, experimental errors or a novelty. An outlier is an observation (or measurement) that is different with respect to the other values contained in a given dataset. Outliers can be due to several…

Iris Recognition System

The pressures on today’s system administrators to have secure systems are ever increasing. One area where security can be improved is in authentication. Iris recognition, a biometric, provides one of the most secure methods of authentication and identification thanks to the unique characteristics of the iris. The iris recognition is now becoming a common authentication method in handheld consumer electronics devices, such as cellphones and tablets. The iris being a biometric parameter is a way better than password protection because of its uniqueness for each individual. General Overview of Iris Recognition System In today’s information technology world, security for systems is becoming more and more important. The number of systems that have been compromised is ever increasing and authentication plays a major role as a first line of defence against intruders. The three main types of authentication are something you know (such as a password), something you have (such as a card or token), and something you are (biometric). Passwords are notorious for being weak and easily crackable due to human nature and our tendency to make passwords easy to remember or writing them down somewhere easily accessible. Cards and tokens can be presented by anyone and although the token…

Signature Recognition and Applications

Signature is a special case of handwriting which includes special characters and flourishes. Many signatures can be unreadable. They are a kind of artistic handwriting objects. However, a signature can be handled as an image, and hence, it can be recognized using computer vision and artificial neural network techniques. Handwritten signatures are widely utilized as a form of personal recognition. However, they have the unfortunate shortcoming of being easily abused by those who would fake the identification or intent of an individual which might be very harmful. Therefore, the need for an automatic signature recognition system is crucial. Signature Recognition Overview The basic goal of the handwritten signatures is to provide an accurate method in order to verify a person’s identity based on the way in which he/she signs his/her name. Hence for this reason, the handwritten signatures are widely accepted, socially and legally throughout the world. There are basically two types of systems – online and offline. The hand-written signature verification uses the features conveyed by every signatory such that the features considered have a unique understanding and the way of signing presents the behavioral biostatistics. Some researchers considered common issues with the extraction of identification data from different…

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