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Mineknowledge Magazine, Vol. I


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The first volume of the magazine of, a data mining services company

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Mineknowledge Magazine, Vol. I

  1. 1. MINEKNOWLEDGE December 1, 2008 You can visualize data mining as a process of searching for treasure buried in the sand or digging up rock to mine for gold - thus 'mining', but the tools we use do it in a truly systematic and efficient way. The problem with data and why you do care By Anna Skountzou You are flooded with tons of knowledge hidden inside? Or consider as a statistical analysis. data, day by day. how such an input would make Put aside the fluffy terminology Spreadsheets, reports, the difference for you and your and start thinking of rules and surveys, you name it. But company? patterns, all illustrated via you are neither the info We bet that, even if it has expositive writing and junkie nor the number already crossed your mind, you visualization schemes, finally cruncher. You actually have have neither the time nor the contributing the less biased and no time to invest on it, right techniques to exploit this most valuable signals for your while you know that you’re data.  And, till now, you were decisions. wasting precious insights forced to hire a connoisseur of And keep in mind that and significant potential. data analysis, something that latent knowledge presents both a Despair? No. proved to be costly and time- hidden cost and a tremendous The problem is simple. Too consuming. Yes, that was the best opportunity, but there is no need much information in case, typically you just did to be stressed about that spreadsheets or other formats, nothing. anymore, you may now just mine laying around your hard disk or Well, till now. Our team not your knowledge! in the cloud. Have you ever tried only relieves you of all these, but to imagine the amount of also extends what you used to 1
  2. 2. MINEKNOWLEDGE December 1, 2008 Our solution why mineknowledge? By Manos Androulakis Speaking of taking the most out of your data sets, “A miner with a mattock in his we provide you with a rock solid solution. The hand is a very rough way to process goes like this: conceptualize the complexity and state-of-the-art of the 1. You send your data to us. processes we execute. A diverse and extended set of 2. You sit back, breathe some fresh air and exploration and filtering enjoy every moment of your life in between. algorithms, next to a variety of learning and meta-learning 3. You open your inbox and receive the very knowledge and secrets trapped in your data, techniques, are utilized, optimized unveiled. and evaluated, while the problem is a computationally intensive one And here are a few more reasons on why to and demands a highly customized select us. approach. 1. It is easy: Consultants, discussions, So we’re putting human meetings. Forget them all. What you need to intelligence and our high expertise do is just send us an email, with your data set in between of various advanced attached. artificial intelligence algorithms, to 2. It is fast: Within a week, results and the finally provide you with the very very knowledge of your data set will pop up in secrets trapped in your data, your inbox. unveiled.” 3. It is fun: Honestly. Working with tons of data is so much fun, especially when others do George Tziralis all the work for you. 6. It is insightful: You already know that, 4. It is secure: Rest assured that we’ve we give you the most valuable insights on your done our best to keep your data and mining data in return. results safe and private (the latter may not be 7. It is affordable: The cost of a valid in our free services). analysis range, you either pay 5. It is clear: If statistics sound greek to nothing, or €500. you, we’re speaking your language. data stand as the least biased input to deci- sion making, a pure source of insights and knowledge. 2
  3. 3. MINEKNOWLEDGE December 1, 2008 “Think of a simple process. Then, make it simpler. Try to find the steps that are still vague. Cut them off. Finally, ask your grandma what she cannot understand. This is how we make things happen in MIneKnowledge” Iro Zacharidou The process: Hassle free By Eleftheria Kanavou It’s simple. You just email us your data set, in balance sheet, you name it). And there are no limitations in an .xls, .csv or .txt format. And then we take over. the number of columns and rows of your set. Clear enough? Ok, that’s it. As long as you got the data The file should in a form like the one in the figure. Let us set in that format, you just sent it to us with an email at make it even more clear. go(AT) We’ll reply with a confirmation of receiving an appropriate file, plus an invoice via paypal. And, within a week, you’ll have a fully fledged mineknowledge report, waiting in your inbox. We think it’s simple. Pricing There are two pricing plans: FREE: You can have the whole report for free, if your Columns in the data set are attributes, like color, size, value data set is of less than 30 columns (attributes) and 300 rows and purchase decision; whatever your data set is made of. (instances), plus you agree that we may publish the analysis in Attributes may be numeric (numbers), or nominal (one or our blog. In this case, the report may take up to a month. more words). You also need to define the ‘target’ attribute, one or more characteristics you want us to focus on and MAX: No restrictions at all, delivery within a week, full explain its behavior based on all the other attributes. fledged analysis for a €500 flat price. You also may call each row an instance, a case, an example, in other words a discrete set of values of each You may take a look at a typical results attribute (let’s say a person’s reply to a survey, a product’s report in our website, while we do wait for your data sets! characteristics in a list of products, quarter updates in a 3
  4. 4. MINEKNOWLEDGE December 1, 2008 An example: Surveys By Athina Pandi You used to think column graphs and pies But, the question remains. Is that the most as the most insightful views you could you can expect from a data set analysis? Have you expect from a data analysis. You’ll actually gain deep insights from your data? The probably change your mind. Let’s take a answer is a clear no. Let us show you why. What follows is a set of rules that emerge from look at a survey example. a proper analysis, even for a data set as oversimplified as the above example. Try this A simplistic one. Consider the data set graph: described in the previous page. Let’s say it refers to answers gathered through or, maybe this set of rules: a survey, or stored in your enterprise database. A typical analysis will finally come up with some If color = yellow then buy = yes graphs, like the ones following. If color = red then buy = yes If color = white then buy = yes If color = green then buy = no If color = blue then buy = no If color = black then buy = no See the difference between the almost obvious and the really insightful? Go find out more in a complete typical mineknowledge report. Yes, this is what your own data set will look like, just after a week. Still considering it? Check out our blog for more case studies. And send us your data, now. And you’re probably used to consider the analysis contributing a graph like the above as, well, fruitful. Same for the following one. 4
  5. 5. MINEKNOWLEDGE December 1, 2008 A few more words about us By Eirini Lygkoni MineKnowledge is a group of young and passionate data engineers, each of us holding an engineering diploma from NTUA and an MSc or PhD in Applied Math, Statistics or Operations Research. We are located in Athens, Greece and London, UK. • George Tziralis is clearly a data junkie who lives on his mac. In the rare case he’s logged off, he enjoys dancing tango and organizing Open Coffee meetings around Greece. Apart from that, at 26 he is a serial entrepreneur, while he also teaches a data mining post-graduate course via blog and tries to find some time to write up his PhD Thesis on markets for forecasting. • Athina Pandi is -among on her second MSc at Imperial College. Communications & Signal Processing is her late interest, next to statistics, data mining and networks. When she is offline, you may find her in a pub around Hyde park. • Eleftheria Kanavou, with a strong tendency in dancing, is the one who naturally gives rhythm to the whole team. Her research interests include stochastic processes and behavioral statistics, while data mining is the physical outlet of her entrepreneurial attitude.  • Manos Androulakis is the algo geek of the team. The biggest the challenge and the data set, the most determined he is for the next diamond to mine. His expertise lies in the areas of statistical designs, variable selection methods and medical applications, under the prism of data mining of course. • Eirini Lygkoni is the epitome of doing magic under pressure. A multi-tasker by nature, she is data is our passion and mining our joy addicted to statistics and probabilities, while she We literally can’t wait literally can’t wait for the next data set to arrive. At to put our hands on your data; get ready the same time, simplicity is her favorite word and to be impressed -or socializing her selection of choice for her rare free even excited- from the precious insights that time. Enough said.  you’ll receive in just a • Lina Massou stands as the quiet power of the week. team. With a strong background in information theory and cryptography, she definitely is the one to take good care of your data and come up with their very knowledge, unveiled. • Anna Skountzou excels at both statistics research and ecological conscience. That said, she’s definitely the one to look for, when you are looking at extracting patterns to let you put your data into much more efficient use, their green footprint included.  • Iro Zacharidou is a true data nut, next to a party animal, putting her deep statistical expertise aside. If you wonder about the outcome of these MINEKNOWLEDGE coming together, rest assured that the required Athens, Greece | London, UK amount of persistence and professionalism to put your data into investigation will be largely outrun.