Applications and Challenges in Data Mining

Top PDF Applications and Challenges in Data Mining:

Data Mining Challenges With Big Data

Data Mining Challenges With Big Data

Abstract: Data analysis is a clear bottleneck in many applications, both due to lack of scalability of the underlying algorithms and due to the complexity of the data that needs to be analyzed. The value of data explodes when it can be linked with other data, thus data integration is a major creator of value. Since most data is directly generated in digital format today, we have the opportunity and the challenge both to influence the creation to facilitate later linkage and to automatically link previously created data. Data analysis, organization, retrieval, and modeling are other foundational challenges. Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents about the data mining and its challenges with big data.

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Issues and Challenges in Big Data Mining

Issues and Challenges in Big Data Mining

Knowledge discovery (KDD) is a process of unveiling hidden knowledge and insights from a large volume of data [9], which involves data mining as its core and the most challenging and interesting step (while other steps are also indispensable) . Typically, data mining uncovers interesting patterns and relationships hidden in a large volume of raw data, and the results tapped out may help make valuable predictions or future observations in the real world. Data mining has been used by a wide range of applications such as business, medicine, science and engineering. It has led to numerous beneficial services to many walks of real businesses – both the providers and ultimately the consumers of services. Applying existing data mining algorithms and techniques to real-world problems has been recently running into many challenges due to the inadequate scalability (and other limitations) of these algorithms and techniques that do not match the three Vs of the emerging big data. Not only the scale of data generated today is unprecedented, the produced data is often continuously generated in the form of streams that require being processed and mined in (nearly) real time. Delayed discovery of even highly valuable knowledge invalidates the usefulness of the discovered knowledge. Big data not only brings new challenges, but also brings opportunities – the interconnected big data with complex and heterogeneous contents bear new sources of knowledge and insights. Big data would become a useless monster if we don‘t have the right tools to harness its ―wildness‖. We argue to consider big data as greatly expanded assets to human. All what we need then is to develop the right tools for efficient store, access, and analytics (SA2 for short). Current data mining techniques and algorithms are not ready to meet the new challenges of big data. Mining big data demands highly scalable strategies and algorithms, more effective preprocessing steps such as data filtering and integration, advanced parallel computing environments (e.g., cloud Paas and IaaS), and intelligent and effective user interaction.

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Why only data mining? a pilot study on inadequacy and domination of  data mining technology

Why only data mining? a pilot study on inadequacy and domination of data mining technology

In past, we had a question, what happens? But with data mining’ we discover that what will happen and why? (Nakasima et.al 2018 14 ). Data mining integrates the various technologies like statistics, machine learning and databases(Irshad et.al, 2018 15 ). It has applications in different disciplines like medical, financial, defence, intelligence and so on(Sohail et.al, 2019 8 ). The tools of data mining include clustering, classifications, associations and detections (Muhammad et.al, 2017 16 ). From decades, data mining have developed in many ways regarding techniques, which includes extracting association, neural networks, logic programming, rough sets and decision trees(Zhu et.al, 2018 17 ). Furthermore, data mining has gone beyond limits like the relational databases to the text mining and multimedia data(Ristoski et.al 2018 18 ); also it’s involved in the information security and detections(Santis et.al, 2018 19 ). After so many developments, companies are still facing some challenges like Scalability (Sohail et.al, 2017 8 ), but till far data mining is working on the massive quality of datasets and also engaged in working for the Terabyte sizes(Najjar et.al, 2018 20 ). By the enormous growth of data in different disciplines’ the question arise, Can this technology fulfilthe needs in extraction of Petabyte size data? This comes in the limitation and domination of data mining technology (on which this paper is focusing). As data mining involves some algorithms, it is important to understand the limitation of data mining algorithms and tools. Which requires the time and space for the complexity? In example: can these algorithms be completed in time? If the problem is decided, what is the complexity? For future predictions, we need to find out more about the complexity of markets and business(Alonso et.al, 2018 11 ) and how data mining is shining in the financial platforms.

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Survey on data mining methods and applications in healthcare domain sector

Survey on data mining methods and applications in healthcare domain sector

The successful application of data mining in highly visible fields like e-business, marketing and retail in knowledge discovery in databases (KDD) in other industries and sectors. Among these sectors that are just discovering data mining are the fields of medicine and public health. This research paper provides a survey of current techniques of KDD, using data mining tools for healthcare and public health. It also discusses critical issues and challenges associated with The research found a growing number of data mining ters for better health policy-making, detection of disease outbreaks and preventable hospital deaths, and detection of fraudulent insurance claims. Electronic health records (EHRs) are representative examples of multimodal/multisource data texts. The diversity of such information sources and the increasing amounts of medical data produced by healthcare institutes annually, pose

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Big Data - Applications and Challenges

Big Data - Applications and Challenges

Inconsistencies in acquired in big data are non avoidable factor in human behaviours and decision support processes. It is common thing in acquired, processed or represented data. Inconsistency or conflict in Big Data base is harmful because it can cause adverse effect on quality of result of mining or extraction process which is to be used in DSS by MIS. In Big Data inconsistency can be easily captured at various stages and dimensions like in data, Meta data, information or knowledge. So inconsistency creates a big challenge in Big Data mining or analysis. Various tasks are involved in mining or analysis which aimed to support in decision making, prediction, classification of facts, regression and association analysis among data etc. there are different types of inconsistency which has impact on these above mentioned various ambition. So the type of inconsistency is also need to be described.

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Data Mining Patterns New Methods And Applications Pascal Poncelet (2008) pdf

Data Mining Patterns New Methods And Applications Pascal Poncelet (2008) pdf

Biology has been one of the most important driv- ing forces in science in the last century. Genome sequencing and the other important developments in molecular biology have been important con- quests, but innumerable challenges still exist in this field, and it is reasonable to suppose that a very long quest lies ahead. On this subject, during an interview given in 1993, Donald Knuth stated, “Bi- ology is so digital, and incredibly complicated, but incredibly useful. … I can’t be as confident about computer science as I can about biology. Biology easily has 500 years of exciting problems to work on.” In an evolving field such as this, continuously having new data to store and to analyze seems natural. Actually, the amount of available data is growing exponentially, and providing biologists with efficient tools to interpret data is imperative. Computer science can help in providing most ap- propriate answers, and the development of new, interdisciplinary, sciences, such as bioinformatics, is an immediate consequence of that. The intrinsic difficulty in understanding biological processes is connected to the necessity of both analyzing enormous data sets and facing different sorts of specific problems. Therefore, techniques to extract and manage biological information have been developed and implemented in the last few years, for example, developing useful software interfaces allowing for the management of large amounts of data through easy human-machine interactions. Several application programs ac- cessible through Internet are available (e.g., Altschul, Gish, Miller, Myers, & Lipman,1990; Altschul, Madden, Schaffer, Zhang, Anang, & Miller, 1997), and the widespread use of the Web represents a peculiar aspect of bioinformatics. Biological data-sets, such as GenBank (Benson, Mizrachi, Lipman, Ostell, & Wheeler, 2005), PDB (Berman, Bourne, & Westbrook, 2004), and Prosite (Hulo, Sigrist, Le Saux, Langendijk- Genevaux, Bordoli, & Gattiker, 2004), featuring an impressive number of entries and containing

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Applications of Operations Research and Data Mining Techniques in the Healthcare Sector

Applications of Operations Research and Data Mining Techniques in the Healthcare Sector

Data mining (DM), that is the process of observing patterns in large amount data involving methods of machine learning, statistics, and database systems ,has seen a boom in interest in many fields of applications including Healthcare, Defence, Government Policy making etc. The primary reasons for the same can be cited as increasing amount of data available, the growing understanding that deeper analyses are far more valuable than simple summary statistics and advent of technology. Data mining is characterized by the inference of general laws from particular instances. Data mining problems raise interesting challenges for several research domains, such as statistics, information theory, databases, and also for Operations Research (OR), since very large search spaces of solutions and questions that need to be answered.

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Futuristic Use of Data Mining Applications

Futuristic Use of Data Mining Applications

In restorative science there is vast extension for use of information mining. Analysis of diesis, wellbeing mind, tolerant profiling and history era and so on are the couple of illustrations. Mammography is the technique utilized as a part of bosom disease location. Radiologists confront part of challenges in location of tumors that is the reason CAM (Computer Aided Methods) could serves to the medicinal staff. So they can deliver the great nature of the outcome identification. The neural systems with back-spread what's more, affiliation administer digging utilized for tumor arrangement as a part of mammograms. The information mining successfully utilized as a part of the finding of lung anomaly that might be carcinogenic or considerate. The information mining calculations fundamentally diminish patient's dangers and finding costs. Utilizing the expectation calculations the watched forecast exactness was 100% for 91.3% cases. The utilization of information mining in social insurance is the generally utilized utilization of information mining. The therapeutic information is mind boggling and hard to investigate. A REMIND (Reliable Extraction and

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Data Mining Foundations And Practice Tsau Young Lin (2008) pdf

Data Mining Foundations And Practice Tsau Young Lin (2008) pdf

However, patterns with higher dimensionality tend to have less frequencies, so using the same threshold value for all patterns risks losing patterns in higher dimensional spaces. Furthermore, patterns with the same dimensionality may need different frequency threshold values for various reasons. For example, a pattern with higher frequency in very dense dimensions may not be as informative and interesting as a pattern with lower frequency in very sparse dimensions. Setting a relatively high frequency threshold tends to bias the search algorithm to favor patterns in dense subspaces only, while patterns in less dense subspaces are neglected. Consider the example shown in Table 1. Each column denotes one of the six attributes (a, b, c, d, e, f), and each row denotes one object (data point). An entry ‘1’ in row i and column j denotes that object i has attribute j. There is a pattern in subspace { abc } that contains two instances { 9, 10 } , and subspace { def } has another pattern containing three instances { 4, 5, 6 } . If we set the minimum frequency threshold to be 3, we lose the pattern in { abc } . However, this pattern in { abc } maybe more interesting than the one in { def } , considering the fact that the number of ‘1’s in attributes a, b, c is much smaller than in attributes d, e, f . Actually, all instances that have entry ‘1’ in a also have entry ‘1’ in b and c, and this may suggest a strong correlation between a, b, c, and also a strong correlation between instances 9 and 10. On the other hand, although the pattern in { def } has a larger frequency, it does not suggest such strong correlations either between attributes d, e, f or between instances 4–6. So we suggest that smaller frequency threshold should be chosen for subspaces with lower densities, that is, subspaces with less number of ‘1’ entries.

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Data Mining A Heuristic Approach Abbass HA (2002) pdf

Data Mining A Heuristic Approach Abbass HA (2002) pdf

classifiers are worth no reward at all. Taking again S y as the key quantity, a patient reinforcement policy reinforces all rules with scores below certain system threshold γ > 0: their κ counters are increased just as if z MIX had been successful. A potentially important distinction is therefore made between, say, classifiers whose second MAP class is correct and classifiers assigning very low probability to the observed output label. The main idea is to help rules with promising low scores to survive until a sufficient number of them are found and maintained. In this case they will hopefully begin to work together as a team and thus they will get their reward from correct z MIX ! The resulting reward scheme is thus parameterized by p > 0 and γ ≥ 0. Since the number of matched classifiers per cycle (m) may be rather large, a reward policy reinforcing a single classifier might appear rather “greedy”. For this reason, higher values of p are typically tried out. The higher p, the easier the cooperation among classifiers (match sets are just provided as more resources to establish themselves). On the other hand, if p is too high then less useful units may begin to be rewarded and the population may become too large. Moderate values of p usually give good results in practice. Parameter γ must also be controlled by monitoring the actual number of units rewarded at a given γ. Again, too generous γ may inflate the population excessively. It appears that some data sets benefit more from γ > 0 than others, the reasons having to do with the degree of overlap among data categories (see table).

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Applications and Trends in Data Mining

Applications and Trends in Data Mining

The advent of Computing Technology has significantly influenced our lives and two major impacts of this effect are Business Data Processing and Scientific Computing. During the early years of the development of computer techniques for business, computer professionals were concerned with designing files to store the data so that information could be efficiently retrieved. There were restrictions on storage size for storing data and on the speed of accessing the data. Needless to say, the activity was restricted to a very few, highly qualified professional. Then came an era when Database management System simplified the task. The responsibility of intricate tasks, such as declarative aspects of the programs was passed on to the database administrator and the user could pose his query in simpler languages such as query languages. Thus almost any business-small, medium or large scale began using computers for day-to-day activities.

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Data Mining Methods And Models Larose DT (2006) pdf

Data Mining Methods And Models Larose DT (2006) pdf

The WEKA (Waikato Environment for Knowledge Analysis) machine learning work- bench is open-source software issued under the GNU General Public License, which includes a collection of tools for completing many data mining tasks. Data Min- ing Methods and Models presents several hands-on, step-by-step tutorial exam- ples using WEKA 3.4, along with input files available from the book’s compan- ion Web site www.dataminingconsultant.com. The reader is shown how to carry out the following types of analysis, using WEKA: logistic regression (Chapter 4), naive Bayes classification (Chapter 5), Bayesian networks classification (Chap- ter 5), and genetic algorithms (Chapter 6). For more information regarding Weka, see http://www.cs.waikato.ac.nz/ ∼ ml/. The author is deeply grateful to James Steck for providing these WEKA examples and exercises. James Steck (james steck@comcast.net) served as graduate assistant to the author during the 2004–2005 academic year. He was one of the first students to complete the master of science in data mining from Central Connecticut State University in 2005 (GPA 4.0) and received the first data mining Graduate Academic Award. James lives with his wife and son in Issaquah, Washington.

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Data Mining Know It All Soumen Chakrabarti (2009) pdf

Data Mining Know It All Soumen Chakrabarti (2009) pdf

Market basket analysis is the use of association techniques to find groups of items that tend to occur together in transactions, typically supermarket checkout data. For many retailers, this is the only source of sales information that is available for data mining. For example, automated analysis of checkout data may uncover the fact that customers who buy beer also buy chips, a discovery that could be significant from the supermarket operator’s point of view (although rather an obvious one that probably does not need a data mining exercise to discover). Or it may come up with the fact that on Thursdays, customers often purchase diapers and beer together, an initially surprising result that, on reflection, makes some sense as young parents stock up for a weekend at home. Such information could be used for many purposes: planning store layouts, limiting special discounts to just one of a set of items that tend to be purchased together, offering coupons for a matching product when one of them is sold alone, and so on. There is enormous added value in being able to identify individual customer’s sales histories. In fact, this value is leading to a proliferation of discount cards or “loyalty” cards that allow retailers to identify individual customers whenever they make a purchase; the personal data that results will be far more valuable than the cash value of the discount. Identification of individual customers not only allows historical analysis of purchasing patterns but also permits precisely targeted special offers to be mailed out to prospective customers.

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Data Mining Practical Machine Learning Tools and Techniques 3rd Edition Mantesh pdf

Data Mining Practical Machine Learning Tools and Techniques 3rd Edition Mantesh pdf

The Weka system that illustrates the ideas in this book forms a crucial component of it. It was conceived by the authors and designed and implemented principally by Eibe Frank, Mark Hall, Peter Reutemann, and Len Trigg, but many people in the machine learning laboratory at Waikato made significant early contributions. Since the first edition of this book, the Weka team has expanded considerably: So many people have contributed that it is impossible to acknowledge everyone properly. We are grateful to Remco Bouckaert for his Bayes net package and many other contribu- tions, Lin Dong for her implementations of multi-instance learning methods, Dale Fletcher for many database-related aspects, James Foulds for his work on multi- instance filtering, Anna Huang for information bottleneck clustering, Martin Gütlein for his work on feature selection, Kathryn Hempstalk for her one-class classifier, Ashraf Kibriya and Richard Kirkby for contributions far too numerous to list, Niels Landwehr for logistic model trees, Chi-Chung Lau for creating all the icons for the Knowledge Flow interface, Abdelaziz Mahoui for the implementation of K*, Stefan Mutter for association-rule mining, Malcolm Ware for numerous miscellaneous contributions, Haijian Shi for his implementations of tree learners, Marc Sumner for his work on speeding up logistic model trees, Tony Voyle for least-median-of- squares regression, Yong Wang for Pace regression and the original implementation of M5 ′ , and Xin Xu for his multi-instance learning package, JRip, logistic regression, and many other contributions. Our sincere thanks go to all these people for their dedicated work, and also to the many contributors to Weka from outside our group at Waikato.

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Dunham   Data Mining pdf

Dunham Data Mining pdf

The time complexity of CURE is O (n2 lg n), while space is O (n). This is worst­ case behavior. The improvements proposed for main memory processing certainly improve on this time complexity because the entire clustering algorithm is performed against only the sample. When clustering is performed on the complete database, a time complexity of only O (n) is required. A heap and k-D tree data structure are used to ensure this performance. One entry in the heap exists for each cluster. Each cluster has not only its representative points, but also the cluster that is closest to it. Entries in the heap are stored in j ncreasing order of the distances between clusters. We assume that each entry u in the heap contains the set of representative points, u. rep; the mean of the points in the cluster, u.mean; and the cluster closest to it, u.closest. We use the heap operations: heapify to create the heap, min to extract the minimum entry in the heap, insert to add a new entry, and delete to delete an entry. A merge procedure is used to merge two clusters. It determines the new representative points for the new cluster. The basic idea of this process is to first find the point that is farthest from the mean. Sub­ sequent points are then chosen based on being the farthest from those points that were previously chosen. A predefined number of points is picked. A k-D tree is a balanced binary tree that can be thought of as a generalization of a binary search tree. It is used to index data of k dimensions where the i1h level of the tree indexes the i1h dimension. In CURE, a k-D tree is used to assist in the merging of clusters. It stores the representative points for each cluster. Initially, there is only one representative point for each cluster, the sole item in it. Operations performed on the tree are: delete to delete an entry form the tree, insert to insert an entry into it, and build to initially create it. The hierarchical clustering algorithm itself, which is from [GRS98], is shown in Algorithm 5. 12. We do not include here either the sampling algorithms or the merging algorithm.

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Data Mining and its Various Concepts

Data Mining and its Various Concepts

firm. Affiliation or individual to lead business over an electronic framework, frequently the E- exchange is creating and winning omnipresence around the world in view of its distinctive purposes of intrigue like negligible exertion, it's favorable nature and besides it's brisk, safe, and strong trade and moreover it is free from prerequisite of time and space. Information mining in internet business is

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Advances in Data Mining: Healthcare Applications

Advances in Data Mining: Healthcare Applications

3. DATA MINING APPLICATION IN HEALTHCARE As mentioned before, in healthcare sector there is a vast scope for data mining techniques to improve the medical science, and also the overall system. Nevertheless, research is medical science not only limited to the invention of new medicines (drugs) or advance instruments and techniques for disease identification, but also there are several other important things. For example, creating a data sheet for each patient including personal information, updating the entry on each visit etc. However, the creation of a patient profile, healthcare, diagnosis of disease is only a few examples of data mining application in healthcare [4]. Data mining in healthcare system indeed require significant effort because the data is complex, various types of data are related to healthcare system [10], [11]. Fuzzy based Neural Networks, Fuzzy logic, Genetic Algorithms, Artificial Neural Network, Nearest neighbor method, Decision trees, Bayesian Belief Networks, and Support Vector Machines are the commonly used techniques for data mining in healthcare sector [6],[11], [12].

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APPLICATION OF DATA MINING IN HEALTH CARE

APPLICATION OF DATA MINING IN HEALTH CARE

Data mining is to extract meaningful and useful information from the large databases. Data Mining has attracted great attention from various fields due to wide and large data present in these fields. To convert these large data into useful information and knowledge data mining is required. The information and knowledge gained by data mining and their applications can be used in various areas including market analysis, Business and E-Commerce fraud detection, customer retention, production control, Scientific, Engineering, and Health Care etc. Various data mining techniques can be applied in various fields.

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HIGHER EDUCATION SYSTEM USING DATA MINING METHODS

HIGHER EDUCATION SYSTEM USING DATA MINING METHODS

Data mining is a means of automating part of process to detect interpretable patterns; it helps us see the forest without getting lost in the trees.Discovering information from data takes two major forms: description and prediction. At the scale we are talking about, it is hard to know what the data shows. Data mining is used to simplify and summarize the data in a manner that we can understand, and then allow us to infer things about specific cases based on the patterns we have observed. Of course, specific applications of data mining methods are limited by the data and computing power available, and are tailored for specific needs and goals. However, there are several main types of pattern detection that are commonly used. Data mining, in this way, can grant immense inferential power. If an algorithm can correctly classify a case into known category based on limited data, it is possible to estimate a wide-range of other information about that case based on the properties of all the other cases in that category. This may sound dry, but it is how most successful Internet companies make their money and from where they draw their power.

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Data Mining Cookbook Robert Elliot (2001) pdf

Data Mining Cookbook Robert Elliot (2001) pdf

First Credit Card Bank wants to predict which customers are going to pay off their balances in the next three months. Once they are identified, First will perform a risk assessment to determine if it can lower their annualized percentage rate in an effort to keep their balances. Through analysis, First has determined that there is some seasonality in balance behavior. For example, balances usually increase in September and October due to school shopping. They also rise in November and December as a result of holiday shopping. Balances almost always drop in January as customers pay off their December balances. Another decrease is typical in April when customers receive their tax refunds. In order to capture the effects of seasonality, First decided to look at two years of data. It restricted the analysis to customers who were out of their introductory period by at least four months. The analysts at First structured the data so that they could use the month as a predictor along with all the behavioral and demographic characteristics on the account. The modeling data set was made up of all the attriters and a random sample of the nonattriters.

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