A merger of (at least) four disciplines. A merger of (at least) four disciplines

A merger of (at least) four disciplines.

• A merger of (at least) four disciplines.

Database queries can be considered to confirm answers to fairly well formed questions or provide simple answers to (relatively) simple questions.

• Database queries can be considered to confirm answers to fairly well formed questions or provide simple answers to (relatively) simple questions.

• Data Analysis is used to give answers to questions which might require some discussion or where the answer is at first vague.

• Data Mining allows the question itself to be ill-formed. “Tell me something interesting about …”

Data Mining is the term used to describe the algorithms/routines used to discover interesting aspects about a dataset.

• Data Mining is the term used to describe the algorithms/routines used to discover interesting aspects about a dataset.

• Knowledge Discovery is the term used to describe the overarching discovery process.

• The difference is similar to the difference between programming and software engineering.

• The terminology is misused (and misappropriated) quite a bit.

• DMKD is one of the hottest research topic to emerge in the database research area in some years.

Major Conferences

• Major Conferences

• ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, SIGKDD
• IEEE International Conference on Data Mining, ICDM
• European Conference on Principles of Data Mining and Knowledge Discovery, PKDD
• Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD
• SIAM International Conference on Data Mining
• International Conference on Data Warehousing and Knowledge Discovery, DaWaK
• … plus local conferences such as AusDM

• Conferences that have many DMKD papers

• ACM SIGMOD International Conference on the Management of Data, SIGMOD
• International Conference on Information and Knowledge Management, CIKM
• International Conference on Very Large Data Bases, VLDB
• IEEE International Conference on Data Engineering, ICDE

• Journals

• Data Mining and Knowledge Discovery, DMKD
• ACM Transactions on Knowledge Discovery from Data, TOKDD
• ACM Transactions on Database Systems, TODS
• IEEE Transactions on Knowledge and Data Engineering, TKDE
• Knowledge and Intelligent Systems, KAIS
• Data and Knowledge Engineering, DKE

Knowledge of Database Systems, Artificial Intelligence, Statistics and Visualisation is not required for this topic.

• Knowledge of Database Systems, Artificial Intelligence, Statistics and Visualisation is not required for this topic.

• HOWEVER, if you find something a little difficult as a result of not having studied it, do read up on it. I will try and provide references.

• Being such a new area, some of the subject matter will come direct from research material. Ie. do not expect to find all of the things we talk about implemented in commercial systems yet.

• Enormous scope to join the team at Flinders in doing postdoctoral, postgraduate or adjunct research.

SAM has important details - please read

• SAM has important details - please read

• Assignments

• I’ve kept it simple.
• You can do all of them and get best of them - but be strategic.

• Tutorial/Discussions Sessions

• Will start in week 3

Timetable

• Timetable

• Thursdays for 13 weeks
• Lectures.
• 3pm – 5pm, 1 hr 50 mins
• Tonsley 1.03
• Tutorial - Starting wk 3.
• noon – 1pm, 50 mins
• Tonsley 1.14

• Text Book

• Tan, Steinbach and Kumar - worth the investment but not critical to buy
• Other resources available in various University libraries

Any two of…

• Any two of…

• Assignment 1 - The development of a data mining or rule visualisation routine
• Assignment 2 - A research based paper
• Assignment 3 - A critique of a seminal DMKD paper

In 1938 Benford noticed that pages of logarithms corresponding to numbers starting with the numeral 1 were much dirtier than other pages.

• In 1938 Benford noticed that pages of logarithms corresponding to numbers starting with the numeral 1 were much dirtier than other pages.

• The Theory …

• Ask anyone to choose numbers randomly and, over a largish number of numbers, there will be
• 1/9th starting with 1,
• 1/9th starting with 2, etc.

We can therefore tell if something that was supposed to be naturally occurring has been faked. For example,

• We can therefore tell if something that was supposed to be naturally occurring has been faked. For example,

• the numbers in an audited set of accounts …
• random samples from a day's stock quotations,
• a tournament's tennis scores,
• the numbers on the front page of The New York Times,
• the populations of towns,
• the molecular weights of compounds,
• the half-lives of radioactive atoms…
•