computer science

Special Interest Group on Knowledge Discovery and Data Mining

Special Interest Group on Knowledge Discovery and Data Mining is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Special Interest Group on Knowledge Discovery and Data Mining rather than just read about it. In short: SIGKDD, representing the Association for Computing Machinery's (ACM) Special Interest Group (SIG) on Knowledge Discovery and Data Mining, hosts an influential annual conference. Conference history The KDD Conference grew from KDD (Knowledge Discovery and Data Mining) workshops at AAAI conferences, which were started by Gregory I.

Key takeaways

Special Interest Group on Knowledge Discovery and Data Mining belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Special Interest Group on Knowledge Discovery and Data Mining to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Special Interest Group on Knowledge Discovery and Data Mining from memory before moving on to harder problems.

Reference excerpt

SIGKDD, representing the Association for Computing Machinery's (ACM) Special Interest Group (SIG) on Knowledge Discovery and Data Mining, hosts an influential annual conference.

Conference history The KDD Conference grew from KDD (Knowledge Discovery and Data Mining) workshops at AAAI conferences, which were started by Gregory I. Piatetsky-Shapiro in 1989, 1991, and 1993, and Usama Fayyad in 1994. Conference papers of each proceedings of the SIGKDD International Conference on Knowledge Discovery and Data Mining are published through ACM. KDD is widely considered the most influential forum for knowledge discovery and data mining research.

The KDD conference has been held each year since 1995, and SIGKDD became an official ACM Special Interest Group in 1998. Past conference locations are listed on the KDD conference web site. The annual ACM SIGKDD conference is recognized as a flagship venue in the field. Based on statistics provided by independent researcher Lexing Xie in her analysis “Visualizing Citation Patterns of Computer Science Conferences“ as part of the research in Computation Media Lab at Australian National University:

4489 papers were published at ACM SIGKDD conference over in the years 1994–2015 (inclusive). These 4489 papers had received 112570 citations in total across 3033 venues. 56% of these 3033 venues are recognized as top 25 venues in the field. The annual conference of ACM SIGKDD has received the highest rating A* from independent organization Computing Research and Education (a.k.a. CORE).

Selection Criteria Like all flagship conferences, SIGKDD imposes a high requirement to present and publish submitted papers. The focus is on innovative research in data mining, knowledge discovery, and large-scale data analytics. Papers emphasizing theoretical foundations are particularly encouraged, as are novel modeling and algorithmic approaches to specific data mining problems in scientific, business, medical, and engineering applications. Visionary papers on new and emerging topics are particularly welcomed. Authors are explicitly discouraged from submitting papers that contain only incremental results or that do not provide significant advances over existing approaches. In 2014, over 2,600 authors from at least fourteen countries submitted over a thousand papers to the conference. A final 151 papers were accepted for presentation and publication, representing an acceptance rate of 14.6%. This acceptance rate is slightly lower than those of other top computer science conferences, which typically have a rate of 15–25%. The acceptance rate of a conference is only a proxy measure of its quality. For example, in the field of information retrieval, the WSDM conference has a lower acceptance rate than the higher-ranked SIGIR.

Awards The group recognizes members of the KDD community with its annual Innovation Award and Service Award. Each year KDD presents a Best Paper Award to recognizes papers presented at the annual SIGKDD conference that advance the fundamental understanding of the field of knowledge discovery in data and data mining. Two research paper awards are granted: Best Research Paper Award Recipients and Best Student Paper Award Recipients.

Best Paper Award (Best Research Track Paper) Winning the ACM SIGKDD Best Paper Award (Best Research Track Paper) is widely considered an internationally recognized significant achievement in a researcher's career. Authors compete with established professionals in the field, such as tenured professors, executives, and eminent industry experts from top institutions. It is common to find press articles and news announcements from the awardees’ institutions and professional media to celebrate this achievement. This award recognizes innovative scholarly articles that advance the fundamental understanding of the field of knowledge discovery in data and data mining. Each year, the award is given to authors of the strongest paper by this criterion, selected by a rigorous process.

Selection Process The selection process follows multiple rounds of peer reviews under stringent criteria. The selection committee consists of leading experts who provide insightful and independent analysis on the merits and degree of innovation of the scholarly articles submitted by each author. The reviewers are required to be recognized subject experts who had extensive contributions to the specific subject area addressed by the paper. Reviewers are also required to be completely unaffiliated with the authors. First, all papers submitted to the ACM SIGKDD conference are reviewed by research track program committee members. Each submitted paper is extensively reviewed by multiple committee members and detailed feedback is given to each author. After review, decisions are made by the committee members to accept or reject the paper based on the paper’s novelty, technical quality, potential impact, clarity, and whether the experimental methods and results are clear, well executed, and repeatable. During the process, committee members also evaluate the merits of each paper based on above factors, and make decision on recommending candidates for Best Paper Award (Best Research Track Paper). The candidates for Best Paper Award (Best Research Track Paper) are extensively reviewed by conference chairs and the best paper award committee. The final determination of the award is based on the level of advancement made by authors through the paper to the understanding of the field of knowledge discovery and data mining. Authors of a single paper who are judged to have contributed the highest level of advancement to the field are selected as recipients of this award. Anyone who submits a scholarly article to SIGKDD is considered for this award.

Previous winners The ACM SIGKDD Best Paper Award (Best Research Track Paper) was given to 49 individuals between 1997 and 2014. Among these individuals, most are distinguished persons and established professionals with celebrated careers, who have made significant contributions to the field.

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Special Interest Group on Knowledge Discovery and Data Mining

Start with the simplest possible case. Write down what Special Interest Group on Knowledge Discovery and Data Mining claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Special Interest Group on Knowledge Discovery and Data Mining before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Special Interest Group on Knowledge Discovery and Data Mining ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Special Interest Group on Knowledge Discovery and Data Mining

In research: Special Interest Group on Knowledge Discovery and Data Mining appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Special Interest Group on Knowledge Discovery and Data Mining in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Special Interest Group on Knowledge Discovery and Data Mining is common in secondary-school and first-year university syllabi. It links to neighbouring topics Association for Computing Machinery Special Interest Groups, Data mining, so understanding it makes those chapters shorter.
In everyday life: Look for Special Interest Group on Knowledge Discovery and Data Mining outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Special Interest Group on Knowledge Discovery and Data Mining in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Special Interest Group on Knowledge Discovery and Data Mining means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Special Interest Group on Knowledge Discovery and Data Mining out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Special Interest Group on Knowledge Discovery and Data Mining in simple terms?

SIGKDD, representing the Association for Computing Machinery's (ACM) Special Interest Group (SIG) on Knowledge Discovery and Data Mining, hosts an influential annual conference. Conference history The KDD Conference grew from KDD (Knowledge Discovery and Data Mining) workshops at AAAI conferences…

Why does Special Interest Group on Knowledge Discovery and Data Mining matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Special Interest Group on Knowledge Discovery and Data Mining?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Special Interest Group on Knowledge Discovery and Data Mining.