Category Archives: Data Mining
Six important skills to become a succesful researcher | The Data Mining …
Today, I will discuss how to become a good researcher and what are the most important skills that a researcher should have. This blog post is aimed at young Master degree students and Ph.D students, to provide some useful advice to them.
1) Being humble and open to criticism
An important skill to be a good researcher is to be humble and to be able to listen to others. Even when a researcher works very hard and think that his/her project is perfect, there are always some flaws or some possibilities for improvement.
A humble researcher will listen to the feedback and opinions of other researchers on their work, whether this feedback is positive or negative, and will think about how to use this feedback to improve their work. A researcher that works alone can do an excellent work. But by discussing research with others, it is possible to get some new ideas. Also, when a researcher present his/her work to others, it is possible to better understand how people will view your work. For example, it is possible that other people will misunderstand your work because something is unclear. Thus, the researcher may need to make adjustments to his research project.
2) Building a social network
A second important thing to work on for young researchers is to try to build a social network. If a researcher has opportunities to attend international conferences, s/he should try to meet other students/professors to establish contact with other researchers. Other ways of establishing contact with other researchers are to send e-mails to ask questions or discuss research, or it could also be at a regional or national level by attending seminars at other universities.
Building a social network is very important as it can create many opportunities for collaborations. Besides, it can be useful to obtain a Ph.D position at another university (or abroad), a post-doctoral research position or even a lecturer position or professor position in the future, or to obtain some other benefits such as being invited to give a talk at another university or being part of the program committee of conferences andworkshops.A young researcher has to oftenwork by herself/himself. But he should also try to connect with other researchers.
For example, during my Ph.D. in Canada, I established contact with some researchers in Taiwan, and I then applied there for doing my postdoc. Then, I used some other contacts recently to find a professor position in China, where I then applied and got the job. Also, I have done many collaborations with other researchers that I have met at conferences.
3) Working hard, working smart
To become a good researcher, another important skill is to spend enough time on your project. In other words, a successful researcher will work hard. For example, it is quite common that good researchers will work more than 10 hours a day. But of course, it is not just about working hard, but also about working smart, that is a researcher should spend each minute of his time to do something useful that will make him/her advance toward his goals. Thus, working hard should be done also with a good planning.
When I was a MSc and Ph.D. student, I could easily work more than 12 hours a day. Sometimes, I would only take a few days off during the whole year. Currently, I still work very hard every day but I have to take a little it more time off due to having a family. However, I have gained in efficiency. Thus, even by working a bit less, I can be much more productive than I was a few years ago.
4) Having clear goals / being organized / having a good research plan
A researcher should also have clear goals. For a Ph.D or MSc student, this includes having a general goal of completing the thesis, but also some subgoals or milestones to attain hismain goal. One should also try to set dates for achieving these goals. In particular, a student should also think about planning their work in terms of deadlines for conferences. It is not always easy to plan well. But it is a skill that one should try to develop. Finally, one should also choose his research topic(s) well to work on meaningful topics that will lead tomaking a good research contribution.
5) Stepping out of thecomfort zone
A young researcher should not be afraid to step out of his comfort zone. This includes trying to meet other researchers, trying to establish collaborations with other researchers, trying to learn new ideas or explore new and difficult topics, and also to study abroad.
For example, after finishing my Ph.D. in Canada, which was mostly related to e-learning, I decided to work on the design of fundamental data mining algorithms for my post-doctoral studies and to do this in Taiwan in a data mining lab. This was a major change both in terms of research area but also in terms of country. This has helped me to build some new connections and alsoto work in amore popular research area, to have more chance ofobtaining a professor position, thereafter. This was risky, but I successfully made the transition.Then, after my postdocI got a professorjob in Canada in auniversity far away from my hometown. This was a compromise that I had to make to be able to get a professor position since there are very few professor positions available in Canada (maybe only 5 that I could apply for every year). Then, after working as a professor for 4 years in Canada, I decided to take another major step out of my comfort zone by selling my house and acceptinga professor job at a top 9 university in China. This last move was very risky as I quit mygood job in Canada where I was going to become permanent. Moreover, I did that before I actually signed the papers for my job in China. And also from a financial perspective Ilost more than 20,000 $ by selling my house quickly to move out. However, the move to Chinahas paid off, as in the next months, I got selectedby a nationalprogram for young talents in China. Thus, I now receiveabout 10 times the funding that I had in Canada for my research, and my salary is more than twice my salary as a professor in Canada, thus covering all the money that I had lost by selling my house. Besides, I have been promoted to full professor and will lead a research center. This is an example of how one can create opportunities in his career by taking risks.
6) Having good writing skills
A young researcher should also try to improve his writing skills. This is very important for all researchers, because a researcher will have to write many publications during his career. Every minute that one spends on improving writing skills will pay off sooner or later.
In terms of writing skills, there are two types of skills.
These skills are acquired by writing and reading papers, and spending the time to improve yourself when writing (for example by reading the grammar rules when unsure about grammar).
Personally, I am not a native English speaker. I have thus worked hard during my graduate studies to improve my English writing skills.
Conclusion
In this brief blog post, I gave some general advice about important skills for becoming a successfulresearcher. I you think that I have forgotten something, please post it as a comment below.
==Philippe Fournier-Vigeris a full professorand the founder of theopen-source data mining software SPMF,offering more than 110data mining algorithms.If you like this blog, you can tweet about it and/or subscribe to my twitter account@philfvto get notified about new posts.
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Six important skills to become a succesful researcher | The Data Mining ...
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Cryptocurrency Mining Hardware Market Size to Grow by USD 12053.16 million From 2022 to 2027, Assessment on Parent Market, Five Forces Analysis,…
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Data Mining – Overview – tutorialspoint.com
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There is a huge amount of data available in the Information Industry. This data is of no use until it is converted into useful information. It is necessary to analyze this huge amount of data and extract useful information from it.
Extraction of information is not the only process we need to perform; data mining also involves other processes such as Data Cleaning, Data Integration, Data Transformation, Data Mining, Pattern Evaluation and Data Presentation. Once all these processes are over, we would be able to use this information in many applications such as Fraud Detection, Market Analysis, Production Control, Science Exploration, etc.
Data Mining is defined as extracting information from huge sets of data. In other words, we can say that data mining is the procedure of mining knowledge from data. The information or knowledge extracted so can be used for any of the following applications
Apart from these, data mining can also be used in the areas of production control, customer retention, science exploration, sports, astrology, and Internet Web Surf-Aid
Data mining is also used in the fields of credit card services and telecommunication to detect frauds. In fraud telephone calls, it helps to find the destination of the call, duration of the call, time of the day or week, etc. It also analyzes the patterns that deviate from expected norms.
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All Resources – Site Guide – NCBI – National Center for Biotechnology …
Assembly
A database providing information on the structure of assembled genomes, assembly names and other meta-data, statistical reports, and links to genomic sequence data.
A curated set of metadata for culture collections, museums, herbaria and other natural history collections. The records display collection codes, information about the collections' home institutions, and links to relevant data at NCBI.
A collection of genomics, functional genomics, and genetics studies and links to their resulting datasets. This resource describes project scope, material, and objectives and provides a mechanism to retrieve datasets that are often difficult to find due to inconsistent annotation, multiple independent submissions, and the varied nature of diverse data types which are often stored in different databases.
The BioSample database contains descriptions of biological source materials used in experimental assays.
A collection of biomedical books that can be searched directly or from linked data in other NCBI databases. The collection includes biomedical textbooks, other scientific titles, genetic resources such as GeneReviews, and NCBI help manuals.
A resource to provide a public, tracked record of reported relationships between human variation and observed health status with supporting evidence. Related information intheNIH Genetic Testing Registry (GTR),MedGen,Gene,OMIM,PubMedand other sources is accessible through hyperlinks on the records.
A registry and results database of publicly- and privately-supported clinical studies of human participants conducted around the world.
A centralized page providing access and links to resources developed by the Structure Group of the NCBI Computational Biology Branch (CBB). These resources cover databases and tools to help in the study of macromolecular structures, conserved domains and protein classification, small molecules and their biological activity, and biological pathways and systems.
A collaborative effort to identify a core set of human and mouse protein coding regions that are consistently annotated and of high quality.
A collection of sequence alignments and profiles representing protein domains conserved in molecular evolution. It also includes alignments of the domains to known 3-dimensional protein structures in the MMDB database.
The dbVar database has been developed to archive information associated with large scale genomic variation, including large insertions, deletions, translocations and inversions. In addition to archiving variation discovery, dbVar also stores associations of defined variants with phenotype information.
An archive and distribution center for the description and results of studies which investigate the interaction of genotype and phenotype. These studies include genome-wide association (GWAS), medical resequencing, molecular diagnostic assays, as well as association between genotype and non-clinical traits.
Includes single nucleotide variations, microsatellites, and small-scale insertions and deletions. dbSNP contains population-specific frequency and genotype data, experimental conditions, molecular context, and mapping information for both neutral variations and clinical mutations.
The NIH genetic sequence database, an annotated collection of all publicly available DNA sequences. GenBank is part of the International Nucleotide Sequence Database Collaboration, which comprises the DNA DataBank of Japan (DDBJ), the European Molecular Biology Laboratory (EMBL), and GenBank at NCBI. These three organizations exchange data on a daily basis. GenBank consists of several divisions, most of which can be accessed through the Nucleotide database. The exceptions are the EST and GSS divisions, which are accessed through the Nucleotide EST and Nucleotide GSS databases, respectively.
A searchable database of genes, focusing on genomes that have been completely sequenced and that have an active research community to contribute gene-specific data. Information includes nomenclature, chromosomal localization, gene products and their attributes (e.g., protein interactions), associated markers, phenotypes, interactions, and links to citations, sequences, variation details, maps, expression reports, homologs, protein domain content, and external databases.
A public functional genomics data repository supporting MIAME-compliant data submissions. Array- and sequence-based data are accepted and tools are provided to help users query and download experiments and curated gene expression profiles.
Stores curated gene expression and molecular abundance DataSets assembled from the Gene Expression Omnibus (GEO) repository. DataSet records contain additional resources, including cluster tools and differential expression queries.
Stores individual gene expression and molecular abundance Profiles assembled from the Gene Expression Omnibus (GEO) repository. Search for specific profiles of interest based on gene annotation or pre-computed profile characteristics.
A collection of expert-authored, peer-reviewed disease descriptions on the NCBI Bookshelf that apply genetic testing to the diagnosis, management, and genetic counseling of patients and families with specific inherited conditions.
Summaries of information for selected genetic disorders with discussions of the underlying mutation(s) and clinical features, as well as links to related databases and organizations.
A voluntary registry of genetic tests and laboratories, with detailed information about the tests such as what is measured and analytic and clinical validity. GTR also is a nexus for information about genetic conditions and provides context-specific links to a variety of resources, including practice guidelines, published literature, and genetic data/information. The initial scope of GTR includes single gene tests for Mendelian disorders, as well as arrays, panels and pharmacogenetic tests.
Contains sequence and map data from the whole genomes of over 1000 organisms. The genomes represent both completely sequenced organisms and those for which sequencing is in progress. All three main domains of life (bacteria, archaea, and eukaryota) are represented, as well as many viruses, phages, viroids, plasmids, and organelles.
The Genome Reference Consortium (GRC) maintains responsibility for the human and mouse reference genomes. Members consist of The Genome Center at Washington University, the Wellcome Trust Sanger Institute, the European Bioinformatics Institute (EBI) and the National Center for Biotechnology Information (NCBI). The GRC works to correct misrepresented loci and to close remaining assembly gaps. In addition, the GRC seeks to provide alternate assemblies for complex or structurally variant genomic loci. At the GRC website (http://www.genomereference.org), the public can view genomic regions currently under review, report genome-related problems and contact the GRC.
A centralized page providing access and links to glycoinformatics and glycobiology related resources.
A database of known interactions of HIV-1 proteins with proteins from human hosts. It provides annotated bibliographies of published reports of protein interactions, with links to the corresponding PubMed records and sequence data.
A collection of consolidated records describing proteins identified in annotated coding regions in GenBank and RefSeq, as well as SwissProt and PDB protein sequences. This resource allows investigators to obtain more targeted search results and quickly identify a protein of interest.
A compilation of data from the NIAID Influenza Genome Sequencing Project and GenBank. It provides tools for flu sequence analysis, annotation and submission to GenBank. This resource also has links to other flu sequence resources, and publications and general information about flu viruses.
Subset of the NLM Catalog database providing information on journals that are referenced in NCBI database records, including PubMed abstracts. This subset can be searched using the journal title, MEDLINE or ISO abbreviation, ISSN, or the NLM Catalog ID.
MeSH (Medical Subject Headings) is the U.S. National Library of Medicine's controlled vocabulary for indexing articles for MEDLINE/PubMed. MeSH terminology provides a consistent way to retrieve information that may use different terminology for the same concepts.
A portal to information about medical genetics. MedGen includes term lists from multiple sources and organizes them into concept groupings and hierarchies. Links are also provided to information related to those concepts in the NIH Genetic Testing Registry (GTR), ClinVar,Gene, OMIM, PubMed, and other sources.
A comprehensive manual on the NCBI C++ toolkit, including its design and development framework, a C++ library reference, software examples and demos, FAQs and release notes. The manual is searchable online and can be downloaded as a series of PDF documents.
Provides links to tutorials and training materials, including PowerPoint slides and print handouts.
Part of the NCBI Handbook, this glossary contains descriptions of NCBI tools and acronyms, bioinformatics terms and data representation formats.
An extensive collection of articles about NCBI databases and software. Designed for a novice user, each article presents a general overview of the resource and its design, along with tips for searching and using available analysis tools. All articles can be searched online and downloaded in PDF format; the handbook can be accessed through the NCBI Bookshelf.
Accessed through the NCBI Bookshelf, the Help Manual contains documentation for many NCBI resources, including PubMed, PubMed Central, the Entrez system, Gene, SNP and LinkOut. All chapters can be downloaded in PDF format.
A project involving the collection and analysis of bacterial pathogen genomic sequences originating from food, environmental and patient isolates. Currently, an automated pipeline clusters and identifies sequences supplied primarily by public health laboratories to assist in the investigation of foodborne disease outbreaks and discover potential sources of food contamination.
Bibliographic data for all the journals, books, audiovisuals, computer software, electronic resources and other materials that are in the library's holdings.
A collection of nucleotide sequences from several sources, including GenBank, RefSeq, the Third Party Annotation (TPA) database, and PDB. Searching the Nucleotide Database will yield available results from each of its component databases.
A database of human genes and genetic disorders. NCBI maintains current content and continues to support its searching and integration with other NCBI databases. However, OMIM now has a new home at omim.org, and users are directed to this site for full record displays.
Database of related DNA sequences that originate from comparative studies: phylogenetic, population, environmental and, to a lesser degree, mutational. Each record in the database is a set of DNA sequences. For example, a population set provides information on genetic variation within an organism, while a phylogenetic set may contain sequences, and their alignment, of a single gene obtained from several related organisms.
A collection of related protein sequences (clusters), consisting of Reference Sequence proteins encoded by complete prokaryotic and organelle plasmids and genomes. The database provides easy access to annotation information, publications, domains, structures, external links, and analysis tools.
A database that includes protein sequence records from a variety of sources, including GenPept, RefSeq, Swiss-Prot, PIR, PRF, and PDB.
A database that includes a collection of models representing homologous proteins with a common function. It includes conserved domain architecture, hidden Markov models and BlastRules. A subset of these models are used by the Prokaryotic Genome Annotation Pipeline (PGAP) to assign names and other attributes to predicted proteins.
Consists of deposited bioactivity data and descriptions of bioactivity assays used to screen the chemical substances contained in the PubChem Substance database, including descriptions of the conditions and the readouts (bioactivity levels) specific to the screening procedure.
Contains unique, validated chemical structures (small molecules) that can be searched using names, synonyms or keywords. The compound records may link to more than one PubChem Substance record if different depositors supplied the same structure. These Compound records reflect validated chemical depiction information provided to describe substances in PubChem Substance. Structures stored within PubChem Compounds are pre-clustered and cross-referenced by identity and similarity groups. Additionally, calculated properties and descriptors are available for searching and filtering of chemical structures.
PubChem Substance records contain substance information electronically submitted to PubChem by depositors. This includes any chemical structure information submitted, as well as chemical names, comments, and links to the depositor's web site.
A database of citations and abstracts for biomedical literature from MEDLINE and additional life science journals. Links are provided when full text versions of the articles are available via PubMed Central (described below) or other websites.
A digital archive of full-text biomedical and life sciences journal literature, including clinical medicine and public health.
A collection of curated, non-redundant genomic DNA, transcript (RNA), and protein sequences produced by NCBI. RefSeqs provide a stable reference for genome annotation, gene identification and characterization, mutation and polymorphism analysis, expression studies, and comparative analyses. The RefSeq collection is accessed through the Nucleotide and Protein databases.
A collection of resources specifically designed to support the research of retroviruses, including a genotyping tool that uses the BLAST algorithm to identify the genotype of a query sequence; an alignment tool for global alignment of multiple sequences; an HIV-1 automatic sequence annotation tool; and annotated maps of numerous retroviruses viewable in GenBank, FASTA, and graphic formats, with links to associated sequence records.
A summary of data for the SARS coronavirus (CoV), including links to the most recent sequence data and publications, links to other SARS related resources, and a pre-computed alignment of genome sequences from various isolates.
The Sequence Read Archive (SRA) stores sequencing data from the next generation of sequencing platforms including Roche 454 GS System, Illumina Genome Analyzer, Life Technologies AB SOLiD System, Helicos Biosciences Heliscope, Complete Genomics, and Pacific Biosciences SMRT.
Contains macromolecular 3D structures derived from the Protein Data Bank, as well as tools for their visualization and comparative analysis.
Contains the names and phylogenetic lineages of more than 160,000 organisms that have molecular data in the NCBI databases. New taxa are added to the Taxonomy database as data are deposited for them.
A database that contains sequences built from the existing primary sequence data in GenBank. The sequences and corresponding annotations are experimentally supported and have been published in a peer-reviewed scientific journal. TPA records are retrieved through the Nucleotide Database.
A repository of DNA sequence chromatograms (traces), base calls, and quality estimates for single-pass reads from various large-scale sequencing projects.
A wide range of resources, including a brief summary of the biology of viruses, links to viral genome sequences in Entrez Genome, and information about viral Reference Sequences, a collection of reference sequences for thousands of viral genomes.
An extension of the Influenza Virus Resource to other organisms, providing an interface to download sequence sets of selected viruses, analysis tools, including virus-specific BLAST pages, and genome annotation pipelines.
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Main Page | Data Mining and Machine Learning
Data Mining and Machine Learning: Fundamental Concepts and Algorithms
Second Edition
Mohammed J. Zaki and Wagner Meira, Jr
Cambridge University Press, March 2020
ISBN: 978-1108473989
The fundamental algorithms in data mining and machine learning form thebasis of data science, utilizing automated methods to analyze patternsand models for all kinds of data in applications ranging from scientificdiscovery to business analytics. This textbook for senior undergraduateand graduate courses provides a comprehensive, in-depth overview of datamining, machine learning and statistics, offering solid guidance forstudents, researchers, and practitioners. The book lays the foundationsof data analysis, pattern mining, clustering, classification andregression, with a focus on the algorithms and the underlying algebraic,geometric, and probabilistic concepts. New to this second edition is anentire part devoted to regression methods, including neural networks anddeep learning.
This second edition has the following new features and content:
New part five on regression: contains chapters on linear regression,logistic regression, neural networks (multilayer perceptrons), deeplearning (recurrent and convolutional neural networks), and regressionassessment.
Expanded material on ensemble models in chapter 24.
Math notation has been clarified, and important equations are nowboxed for emphasis throughout the text.
Geometric view emphasized throughout the text, including for regression.
Errors from the first edition have been corrected.
You can find here the online book,errata, table of contents and resources like slides,videos andother materials for the new edition.
Description of the first edition is alsoavailable.
Mohammed J. Zaki, Rensselaer Polytechnic Institute, New York
Mohammed J. Zaki is Professor of Computer Science at Rensselaer Polytechnic Institute, New York, where he also serves as Associate Department Head and Graduate Program Director. He has more than 250 publications and is an Associate Editor for the journal Data Mining and Knowledge Discovery. He is on the Board of Directors for Association for Computing Machinery's Special Interest Group on Knowledge Discovery and Data Mining (ACM SIGKDD). He has received the National Science Foundation CAREER Award, and the Department of Energy Early Career Principal Investigator Award. He is an ACM Distinguished Member, and IEEE Fellow.
Wagner Meira, Jr, Universidade Federal de Minas Gerais, Brazil
Wagner Meira, Jr is Professor of Computer Science at Universidade Federal de Minas Gerais, Brazil, where he is currently the chair of the department. He has published more than 230 papers on data mining and parallel and distributed systems. He was leader of the Knowledge Discovery research track of InWeb and is currently Vice-chair of INCT-Cyber. He is on the editorial board of the journal Data Mining and Knowledge Discovery and was the program chair of SDM'16 and ACM WebSci'19. He has been a CNPq researcher since 2002. He has received an IBM Faculty Award and several Google Faculty Research Awards.
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