A Brief History of Data Mining and Data Mining Society
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Summary
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Why Data Mining? n The Explosive Growth of Data: from terabytes to petabytes n Data collection and data availability n Automated data collection tools, database systems, Web, computerized society
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Major sources of abundant data n Business: Web, e-commerce, transactions, stocks, …
Society and everyone: news, digital cameras, YouTube
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We are drowning in data, but starving for knowledge!
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“Necessity is the mother of invention”—Data mining—Automated analysis of massive data sets
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Evolution of Sciences: New Data Science Era n Before 1600: Empirical science
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1600-1950s: Theoretical science n n
1950s-1990s: Computational science n n
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Over the last 50 years, most disciplines have grown a third, computational branch
(e.g. empirical, theoretical, and computational ecology, or physics, or linguistics.)
Computational Science traditionally meant simulation. It grew out of our inability to find closed-form solutions for complex mathematical models.
1990-now: Data science n The flood of data from new scientific instruments and simulations
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The ability to economically store and manage petabytes of data online
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The Internet and computing Grid that makes all these archives universally accessible
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Each discipline has grown a theoretical component. Theoretical models often motivate experiments and generalize our understanding.
Scientific info. management, acquisition, organization, query, and visualization tasks scale almost linearly with data volumes
Data mining is a major new challenge!
Jim Gray and Alex Szalay, The World Wide Telescope: An Archetype for Online Science,
Comm. ACM, 45(11): 50-54, Nov. 2002
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Introduction n Why Data Mining?
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What Is Data Mining?
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A Multi-Dimensional View of Data Mining
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What Kind of Data Can Be Mined?
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What Kinds of Patterns Can Be Mined?
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What Technologies Are Used?
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What Kind of Applications Are Targeted?
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Major Issues in Data Mining
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A Brief History of Data Mining and Data Mining Society
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Summary
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What Is Data Mining? n Data mining (knowledge discovery from data) n Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data
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Alternative names n n
Data mining: a misnomer?
Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc.
Watch out: Is everything “data mining”? n Simple search and query processing
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(Deductive) expert systems
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Knowledge Discovery (KDD) Process n n
This is a view from typical database systems and data
Pattern Evaluation warehousing communities
Data mining plays an essential role in the knowledge discovery
Data Mining process Task-relevant Data
Data Warehouse
Selection
Data Cleaning
Data Integration
Databases
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Example: A Web Mining Framework n Web mining usually involves n Data cleaning
Abstract- Outlier detection is an active area for research in data set mining community. Finding outliers from a collection of patterns is a very well-known problem in data mining. Outlier Detection as a branch of data mining has many applications in data stream analysis and requires more attention. An outlier is a pattern which is dissimilar with respect to the rest of the patterns in the data set. Detecting outliers and analyzing large data sets can lead to discovery of unexpected knowledge in area…
Chapter 03: Database Systems, Data Centers, and Business Intelligence TRUE/FALSE 1. A database, a database management system, and the application programs that use the data make up a database environment. ANS: T PTS: 1 REF: Why Learn About Database Systems, Data Centers, And Business Intelligence 2. A database scientist is a skilled and trained IS professional who directs all activities related to an organization’s database, including providing security from intruders. ANS: F PTS: 1 REF:…
4.6 ADVANTAGES Data mining is present in many aspects of our daily lives, whether we realize it or not. It a ects how we shop, work, and search for information, and can even in uence our leisure time, health, and well-being. So data mining is ubiquitous (or ever-present. Several of these examples also represent invisible data mining , in which smart soft- MITCOE, Pune. 18 Dept. of Computer Engg. Student Performance Analysis using Apriori Algorithm ware, such as search engines, customer-adaptive web…
Name ____________________________________ Date ______________ Cookie Mining Lab: APES The purpose of this activity is to simulate a mining operation. In order to make the simulation economically valid, many of the costs associated with real mining operations will be considered. Several of the economic considerations in this simulation follow: ● A land area will be purchased from the bank. ● The land area will be surveyed and quantified. ● Mining equipment will be rented. ● A mining operation will be undertaken, with…
your office network to your home computer to your cell phone. What one might say on their facebook or personal website can be a clue to their state of mind as well as to their overall financial picture. We use data every single day and it provides a vast knowledge base of personal and private data. Interviewing witnesses opens a channel of communications and starts the conversation with witnesses and potential suspects. Documentary evidence is gathered which includes written statements (checks, invoices…
education could influence the regional income and employment. And because Australia has a reputation for ‘exporter of mineral and energy product’, it’s reasonable to assume that those the income level in mining towns might be higher than others. Since the physical requirement for employees engaging in mining industry is above other industries, the demographic profile could be predicted with high percentage of young male adults. Moreover, considering the inconvenience of education in those suburbs, the educational…
education institutes offering distance learning courses through web can use this model to identify which area of their course can be improved by data mining technology to achieve higher student marks. General Terms Pattern Recognition, Data Mining, Algorithms. Keywords Web based learning, performance measures, k means. 1. INTRODUCTION The proliferation of use of data in many application areas such as banking, fraud detection, insurance and medicine is due to the result of powerful, affordable and sustainable…
technology company CareFusion Corporation. CEO confidence in the M&A market has boosted the number of these mega transactions in 2014. Playing right into this mega deal trend is the Energy, Mining & Utilities sector, which saw many of the top transactions during the month. Dominating October M&A activity Energy, Mining & Utilities dominated October 2014’s deal activity. With the concentration of large deals in the sector, it was at the top in terms of the aggregate value of transactions in October, with…
propagation graph, possibly learnt from cascade analysis, is it possible to get a smaller nearly di↵usionequivalent representation for it? Getting a smaller equivalent graph will help multiple algorithmic and data mining tasks like influence maximization, immunization, understanding cascade data and data compression. In this paper, we study a novel graph coarsening problem with the aim of approximating a large social network by a much smaller graph that approximately preserves the network structure.…
