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7,101 | The term "data mining" is a misnomer because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing as well as any application of computer decision sup... |
7,102 | The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records , unusual records , and dependencies . This usually involves using database techniques such as spatial indices. These patterns can then b... |
7,103 | The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hi... |
7,104 | The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new h... |
7,105 | In the 1960s, statisticians and economists used terms like data fishing or data dredging to refer to what they considered the bad practice of analyzing data without an a-priori hypothesis. The term "data mining" was used in a similarly critical way by economist Michael Lovell in an article published in the Review of Ec... |
7,106 | The term data mining appeared around 1990 in the database community, with generally positive connotations. For a short time in 1980s, the phrase "database mining"™, was used, but since it was trademarked by HNC, a San Diego-based company, to pitch their Database Mining Workstation; researchers consequently turned to da... |
7,107 | The manual extraction of patterns from data has occurred for centuries. Early methods of identifying patterns in data include Bayes' theorem and regression analysis . The proliferation, ubiquity and increasing power of computer technology have dramatically increased data collection, storage, and manipulation ability. ... |
7,108 | The knowledge discovery in databases process is commonly defined with the stages: |
7,109 | It exists, however, in many variations on this theme, such as the Cross-industry standard process for data mining which defines six phases: |
7,110 | or a simplified process such as Pre-processing, Data Mining, and Results Validation. |
7,111 | Polls conducted in 2002, 2004, 2007 and 2014 show that the CRISP-DM methodology is the leading methodology used by data miners. |
7,112 | The only other data mining standard named in these polls was SEMMA. However, 3–4 times as many people reported using CRISP-DM. Several teams of researchers have published reviews of data mining process models, and Azevedo and Santos conducted a comparison of CRISP-DM and SEMMA in 2008. |
7,113 | Before data mining algorithms can be used, a target data set must be assembled. As data mining can only uncover patterns actually present in the data, the target data set must be large enough to contain these patterns while remaining concise enough to be mined within an acceptable time limit. A common source for data i... |
7,114 | Data mining involves six common classes of tasks: |
7,115 | Data mining can unintentionally be misused, producing results that appear to be significant but which do not actually predict future behavior and cannot be reproduced on a new sample of data, therefore bearing little use. This is sometimes caused by investigating too many hypotheses and not performing proper statistica... |
7,116 | The final step of knowledge discovery from data is to verify that the patterns produced by the data mining algorithms occur in the wider data set. Not all patterns found by the algorithms are necessarily valid. It is common for data mining algorithms to find patterns in the training set which are not present in the gen... |
7,117 | If the learned patterns do not meet the desired standards, it is necessary to re-evaluate and change the pre-processing and data mining steps. If the learned patterns do meet the desired standards, then the final step is to interpret the learned patterns and turn them into knowledge. |
7,118 | The premier professional body in the field is the Association for Computing Machinery's Special Interest Group on Knowledge Discovery and Data Mining . Since 1989, this ACM SIG has hosted an annual international conference and published its proceedings, and since 1999 it has published a biannual academic journal titl... |
7,119 | Computer science conferences on data mining include: |
7,120 | Data mining topics are also present in many data management/database conferences such as the ICDE Conference, SIGMOD Conference and International Conference on Very Large Data Bases. |
7,121 | There have been some efforts to define standards for the data mining process, for example, the 1999 European Cross Industry Standard Process for Data Mining and the 2004 Java Data Mining standard . Development on successors to these processes was active in 2006 but has stalled since. JDM 2.0 was withdrawn without rea... |
7,122 | For exchanging the extracted models—in particular for use in predictive analytics—the key standard is the Predictive Model Markup Language , which is an XML-based language developed by the Data Mining Group and supported as exchange format by many data mining applications. As the name suggests, it only covers predicti... |
7,123 | Data mining is used wherever there is digital data available. Notable examples of data mining can be found throughout business, medicine, science, finance, construction, and surveillance. |
7,124 | While the term "data mining" itself may have no ethical implications, it is often associated with the mining of information in relation to user behavior . |
7,125 | The ways in which data mining can be used can in some cases and contexts raise questions regarding privacy, legality, and ethics. In particular, data mining government or commercial data sets for national security or law enforcement purposes, such as in the Total Information Awareness Program or in ADVISE, has raised p... |
7,126 | Data mining requires data preparation which uncovers information or patterns which compromise confidentiality and privacy obligations. A common way for this to occur is through data aggregation. Data aggregation involves combining data together in a way that facilitates analysis . This is not data mining per se, but a... |
7,127 | It is recommended to be aware of the following before data are collected: |
7,128 | Data may also be modified so as to become anonymous, so that individuals may not readily be identified. However, even "anonymized" data sets can potentially contain enough information to allow identification of individuals, as occurred when journalists were able to find several individuals based on a set of search hist... |
7,129 | The inadvertent revelation of personally identifiable information leading to the provider violates Fair Information Practices. This indiscretion can cause financial,
emotional, or bodily harm to the indicated individual. In one instance of privacy violation, the patrons of Walgreens filed a lawsuit against the compa... |
7,130 | Europe has rather strong privacy laws, and efforts are underway to further strengthen the rights of the consumers. However, the U.S.–E.U. Safe Harbor Principles, developed between 1998 and 2000, currently effectively expose European users to privacy exploitation by U.S. companies. As a consequence of Edward Snowden's g... |
7,131 | In the United Kingdom in particular there have been cases of corporations using data mining as a way to target certain groups of customers forcing them to pay unfairly high prices. These groups tend to be people of lower socio-economic status who are not savvy to the ways they can be exploited in digital market places. |
7,132 | In the United States, privacy concerns have been addressed by the US Congress via the passage of regulatory controls such as the Health Insurance Portability and Accountability Act . The HIPAA requires individuals to give their "informed consent" regarding information they provide and its intended present and future us... |
7,133 | U.S. information privacy legislation such as HIPAA and the Family Educational Rights and Privacy Act applies only to the specific areas that each such law addresses. The use of data mining by the majority of businesses in the U.S. is not controlled by any legislation. |
7,134 | Under European copyright database laws, the mining of in-copyright works without the permission of the copyright owner is not legal. Where a database is pure data in Europe, it may be that there is no copyright—but database rights may exist, so data mining becomes subject to intellectual property owners' rights that a... |
7,135 | The European Commission facilitated stakeholder discussion on text and data mining in 2013, under the title of Licences for Europe. The focus on the solution to this legal issue, such as licensing rather than limitations and exceptions, led to representatives of universities, researchers, libraries, civil society group... |
7,136 | US copyright law, and in particular its provision for fair use, upholds the legality of content mining in America, and other fair use countries such as Israel, Taiwan and South Korea. As content mining is transformative, that is it does not supplant the original work, it is viewed as being lawful under fair use. For ex... |
7,137 | The following applications are available under free/open-source licenses. Public access to application source code is also available. |
7,138 | Carrot2: Text and search results clustering framework. |
7,139 | Chemicalize.org: A chemical structure miner and web search engine. |
7,140 | ELKI: A university research project with advanced cluster analysis and outlier detection methods written in the Java language. |
7,141 | GATE: a natural language processing and language engineering tool. |
7,142 | KNIME: The Konstanz Information Miner, a user-friendly and comprehensive data analytics framework. |
7,143 | Massive Online Analysis : a real-time big data stream mining with concept drift tool in the Java programming language. |
7,144 | MEPX: cross-platform tool for regression and classification problems based on a Genetic Programming variant. |
7,145 | mlpack: a collection of ready-to-use machine learning algorithms written in the C++ language. |
7,146 | NLTK : A suite of libraries and programs for symbolic and statistical natural language processing for the Python language. |
7,147 | OpenNN: Open neural networks library. |
7,148 | Orange: A component-based data mining and machine learning software suite written in the Python language. |
7,149 | PSPP: Data mining and statistics software under the GNU Project similar to SPSS |
7,150 | R: A programming language and software environment for statistical computing, data mining, and graphics. It is part of the GNU Project. |
7,151 | scikit-learn: An open-source machine learning library for the Python programming language; |
7,152 | Torch: An open-source deep learning library for the Lua programming language and scientific computing framework with wide support for machine learning algorithms. |
7,153 | UIMA: The UIMA is a component framework for analyzing unstructured content such as text, audio and video – originally developed by IBM. |
7,154 | Weka: A suite of machine learning software applications written in the Java programming language. |
7,155 | The following applications are available under proprietary licenses. |
7,156 | For more information about extracting information out of data , see: |
7,157 | The history of garbled circuits is complicated. The invention of garbled circuit was credited to Andrew Yao, as Yao introduced the idea in the oral presentation of a paper in FOCS'86. This was documented by Oded Goldreich in 2003. The first written document about this technique was by Goldreich, Micali, and
Wigderson i... |
7,158 | The oblivious transfer can be built using asymmetric cryptography like the RSA cryptosystem. |
7,159 | The protocol consists of 6 steps as follows: |
7,160 | The Boolean circuit for small functions can be generated by hand. It is conventional to make the circuit out of 2-input XOR and AND gates. It is important that the generated circuit has the minimum number of AND gates . There are methods that generate the optimized circuit in term of number of AND gates using logic syn... |
7,161 | Alice replaced 0 and 1 with the corresponding labels: |
7,162 | After this, Alice randomly permutes the table such that the output value cannot be determined from the row. The protocol's name, garbled, is derived from this random permutation. |
7,163 | This optimization reduces the size of garbled tables from 4 rows to 3 rows. Here, instead of generating a label for the output wire of a gate randomly, Alice generates it using a function of the input labels. She generates the output labels such that the first entry of the garbled table becomes all 0 and no longer need... |
7,164 | Free XOR optimization implies an important point that the amount of data transfer and number of encryption and decryption of the garbled circuit protocol relies only on the number of AND gates in the Boolean circuit not the XOR gates. Thus, between two Boolean circuits representing the same function, the one with the... |
7,165 | This optimization reduce the size of garbled table for AND gates from 3 row in Row Reduction to 2 rows. It is shown that this is the theoretical minimum for the number of rows in the garbled table, for a certain class of garbling techniques. |
7,166 | The Yao's Garbled Circuit is secure against a semi-honest adversary. This type of adversary follows the protocol and does not do any malicious behavior, but it tries to violate the privacy of the other party's input by scrutinizing the messages transmitted in the protocol. |
7,167 | It is more challenging to make this protocol secure against a malicious adversary that deviates from the protocol. One of the first solutions to make the protocol secure against malicious adversary is to use zero-knowledge proof to prevent malicious activities during the protocol. For years, this approach was considere... |
7,168 | In light of the fact that one should be able to generate a proof of some statement only when in possession of certain secret information connected to the statement, the verifier, even after having become convinced of the statement's truth, should nonetheless remain unable to prove the statement to third parties. |
7,169 | In the plain model, nontrivial zero-knowledge proofs demand interaction between the prover and the verifier. This interaction usually entails the selection of one or more random challenges by the verifier; the random origin of these challenges, together with the prover's successful responses to them notwithstanding, j... |
7,170 | In the common random string and random oracle models, non-interactive zero-knowledge proofs exist, in light of the Fiat–Shamir heuristic. These proofs, in practice, rely on computational assumptions . |
7,171 | There is a well-known story presenting the fundamental ideas of zero-knowledge proofs, first published in 1990 by Jean-Jacques Quisquater and others in their paper "How to Explain Zero-Knowledge Protocols to Your Children". The two parties in the zero-knowledge proof story are Peggy as the prover of the statement, and ... |
7,172 | In this story, Peggy has uncovered the secret word used to open a magic door in a cave. The cave is shaped like a ring, with the entrance on one side and the magic door blocking the opposite side. Victor wants to know whether Peggy knows the secret word; but Peggy, being a very private person, does not want to reveal h... |
7,173 | They label the left and right paths from the entrance A and B. First, Victor waits outside the cave as Peggy goes in. Peggy takes either path A or B; Victor is not allowed to see which path she takes. Then, Victor enters the cave and shouts the name of the path he wants her to use to return, either A or B, chosen at ra... |
7,174 | However, suppose she did not know the word. Then, she would only be able to return by the named path if Victor were to give the name of the same path by which she had entered. Since Victor would choose A or B at random, she would have a 50% chance of guessing correctly. If they were to repeat this trick many times, say... |
7,175 | Thus, if Peggy repeatedly appears at the exit Victor names, he can conclude that it is extremely probable that Peggy does, in fact, know the secret word. |
7,176 | One side note with respect to third-party observers: even if Victor is wearing a hidden camera that records the whole transaction, the only thing the camera will record is in one case Victor shouting "A!" and Peggy appearing at A or in the other case Victor shouting "B!" and Peggy appearing at B. A recording of this ty... |
7,177 | Further, if Victor chooses his A's and B's by flipping a coin on-camera, this protocol loses its zero-knowledge property; the on-camera coin flip would probably be convincing to any person watching the recording later. Thus, although this does not reveal the secret word to Victor, it does make it possible for Victor to... |
7,178 | Notice that Peggy could prove to Victor that she knows the magic word, without revealing it to him, in a single trial. If both Victor and Peggy go together to the mouth of the cave, Victor can watch Peggy go in through A and come out through B. This would prove with certainty that Peggy knows the magic word, without re... |
7,179 | Imagine your friend "Victor" is red-green colour-blind and you have two balls: one red and one green, but otherwise identical. To Victor, the balls seem completely identical. Victor is skeptical that the balls are actually distinguishable. You want to prove to Victor that the balls are in fact differently coloured, b... |
7,180 | Here is the proof system. You give the two balls to Victor and he puts them behind his back. Next, he takes one of the balls and brings it out from behind his back and displays it. He then places it behind his back again and then chooses to reveal just one of the two balls, picking one of the two at random with equal p... |
7,181 | By looking at the balls' colours, you can, of course, say with certainty whether or not he switched them. On the other hand, if the balls were the same colour and hence indistinguishable, there is no way you could guess correctly with probability higher than 50%. |
7,182 | Since the probability that you would have randomly succeeded at identifying each switch/non-switch is 50%, the probability of having randomly succeeded at all switch/non-switches approaches zero . If you and your friend repeat this "proof" multiple times , your friend should become convinced that the balls are indeed ... |
7,183 | The above proof is zero-knowledge because your friend never learns which ball is green and which is red; indeed, he gains no knowledge about how to distinguish the balls. |
7,184 | One well-known example of a zero-knowledge proof is the "Where's Waldo" example. In this example, the prover wants to prove to the verifier that they know where Waldo is on a page in a Where's Waldo? book, without revealing his location to the verifier. |
7,185 | The prover starts by taking a large black board with a small hole in it, the size of Waldo. The board is twice the size of the book in both directions, so the verifier cannot see where on the page the prover is placing it. The prover then places the board over the page so that Waldo is in the hole. |
7,186 | The verifier can now look through the hole and see Waldo, but they cannot see any other part of the page. Therefore, the prover has proven to the verifier that they know where Waldo is, without revealing any other information about his location. |
7,187 | This example is not a perfect zero-knowledge proof, because the prover does reveal some information about Waldo's location, such as his body position. However, it is a decent illustration of the basic concept of a zero-knowledge proof. |
7,188 | A zero-knowledge proof of some statement must satisfy three properties: |
7,189 | The first two of these are properties of more general interactive proof systems. The third is what makes the proof zero-knowledge. |
7,190 | We can apply these ideas to a more realistic cryptography application. Peggy wants to prove to Victor that she knows the discrete log of a given value in a given group. |
7,191 | Thus, a cheating prover has a 0.5 probability of successfully cheating in one round. By executing a large enough number of rounds, the probability of a cheating prover succeeding can be made arbitrarily low. |
7,192 | Peggy proves to know the value of x . |
7,193 | The following scheme is due to Manuel Blum. |
7,194 | In this scenario, Peggy knows a Hamiltonian cycle for a large graph G. Victor knows G but not the cycle Finding a Hamiltonian cycle given a large graph is believed to be computationally infeasible, since its corresponding decision version is known to be NP-complete. Peggy will prove that she knows the cycle without si... |
7,195 | To show that Peggy knows this Hamiltonian cycle, she and Victor play several rounds of a game: |
7,196 | It is important that the commitment to the graph be such that Victor can verify, in the second case, that the cycle is really made of edges from H. This can be done by, for example, committing to every edge separately. |
7,197 | If Peggy does know a Hamiltonian cycle in G, she can easily satisfy Victor's demand for either the graph isomorphism producing H from G or a Hamiltonian cycle in H . |
7,198 | Peggy's answers do not reveal the original Hamiltonian cycle in G. Each round, Victor will learn only H's isomorphism to G or a Hamiltonian cycle in H. He would need both answers for a single H to discover the cycle in G, so the information remains unknown as long as Peggy can generate a distinct H every round. If P... |
7,199 | If Peggy does not know the information, she can guess which question Victor will ask and generate either a graph isomorphic to G or a Hamiltonian cycle for an unrelated graph, but since she does not know a Hamiltonian cycle for G she cannot do both. With this guesswork, her chance of fooling Victor is 2−n, where n is t... |
7,200 | Different variants of zero-knowledge can be defined by formalizing the intuitive concept of what is meant by the output of the simulator "looking like" the execution of the real proof protocol in the following ways: |
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