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Munindar P. Singh and Mona Singh Deconstructing the “Any” Key popular techies’ tale goes call this desired behavior pens. Press the caps lock key and as follows. A user calls “acknowledged continuation.” it may affect what you type later, A customer support with a This program might look some- but the program still doesn’t problem: “The program says thing like the following in budge. On some computers, ‘press any key to continue’ but I pseudo code: when the alt key is pressed, can’t find the any key on the key- something weird may happen. board.” <stuff done before> Ditto with the function keys. Stupid user. display(“press any key to continue”); Press the power on-off key, We suppose this story is apoc- get_character() <implied wait> which exists on some keyboards, ryphal. Most people have a rea- <stuff done after> and the results may be far more sonable understanding of dramatic than desired. It is fair language even if they aren’t This is a nice program and to say the program will not con- familiar with computers. But gives every indication of being tinue as promised. we’re not surprised if some user reliable. The program displays a Some key combinations count has had this very difficulty figur- message and awaits an input as one key. Press shift and a letter ing out the “any” key. from the user. As soon as an (say, j), and it works fine, as any Our point is that although an input, any input, arrives, the programmer would expect. How- actual user may or may not be as program can continue. ever, some key combinations are ill-informed as the apocryphal When the user sees the mes- dangerous. In the Windows one, virtually all real program- sage “press any key to continue,” world, the control-alt- delete mers are insensitive. And we he or she (usually) has a readily combination opens up a window don’t mean personal insensitivity. available keyboard. By definition, listing running programs that the We mean technical insensitivity, one would expect, a keyboard is user may kill. A second control- the kind that results from view- something with keys. alt-delete can reset the computer. ing a problem solely from one’s But not all keys are alike. In the Unix world, control-C can own perspective. Experienced users know to press kill a program and control-Z sus- We imagine the programmer a key that works, such as the pends it. Control-S can suspend writing the instructions to press space bar or the enter key. How- any output. any key wants to delay process- ever, this is more a matter of a The gist of this is that a speci- ing until the user has acknowl- user having been trained through fication saying “press any key to edged reading some message. past experience to press the right continue” is far from unambigu- (This is called a dialogue, keys than a matter of the specifi- ous to the user. It may seem clear although it doesn’t allow much in cation itself being clear. from the programmer’s perspec- the way of conversation.) The For instance, some of the keys tive because the program receives programmer writes a simple pro- have no effect. Press the shift or an input only under circum- gram to achieve this effect—let’s control key and nothing hap- stances that the underlying oper- COMMUNICATIONS OF THE ACM April 2000/Vol. 43, No. 4 107 ating system deems appropriate, the users’ perspective. To under- the case of the “any” key, we that is, only when an acceptable stand a communication from the must keep trying. key is pressed. So the right dis- perspective of its recipient is one As for the matter of acknowl- play message ought to be, “press of the lessons of deconstruction edged continuation, just ask the any acceptable key to continue.” theory. user to press the space bar. c However, such an instruction A lot of people talk of the Munindar P. Singh would be meaningless, because importance of being user-cen- ([email protected]) teaches computer science at North Carolina the term “acceptable” has no tered. Here is a case where being State University in Raleigh. obvious interpretation. user-centered has direct conse- Mona Singh ([email protected]) is The moral? We as program- quences on our programs. with the Ericsson New Concepts group in mers should take into account Although thinking from another Research Triangle Park, NC. alternative perspectives, especially perspective is rarely as easy as in © 2000 ACM 0002-0782/00/0400 $5.00 Jerzy W. Grzymala-Busse and Wojciech Ziarko Data Mining and Rough Set Theory his is in response to “Myths applications of rough set theory, existing discretization methods, about Rough Set Theory” discretization is used as a prepro- based on many different T(Nov. 1998, p. 102) by cessing. However, discretization is approaches to uncertainty, could W.W. Koczkodaj, M. Orlowski, required in all rule (or tree) induc- be used as preprocessing for and V.W. Marek. The authors tion systems. Such systems consti- KDD-R and LERS. Discretization raise some important issues and tute the core of data mining (or is a technique used in many areas, express some legitimate concerns. knowledge discovery). Many such including machine learning and We are surprised they list rough well-known systems (such as learning in Bayesian networks, set theory as the only discipline in C4.5, based on conditional and is definitely not restricted to which there are two of the cited entropy or CART, based on Bayes rule induction systems based on problems—the discipline in which rule) are equipped with their own rough set theory. discretization is necessary or which discretization schemes. Neither To illustrate the complexities deals with complex data. The C4.5 nor CART use rough set involved in data analysis, the third problem raised by the theory. Practically every machine authors refer to an example of a authors is associated with the dif- learning system uses discretization table with 10 attributes, each with ference between objective and sub- while very few of them are based 20 values, which is likely to lead jective approaches to uncertainty. on rough set theory. To compli- to a large “number of possible Let us start with discretization. cate matters, discretization meth- instances.” However, one could Many people deal with discretiza- ods used in rule induction systems cite this kind of example to illus- tion unknowingly. For example, in based on rough set theory, such as trate potential problems occurring grading student work, there are KDD-R or LERS, are not based in all disciplines dealing with data, usual cut-points (90% for an “A,” on rough set theory (for example, starting from statistics, through 80% for a “B,” and so forth); orig- both KDD-R and LERS use sta- database management, and ending inal scores are replaced by inter- tistical methods). Furthermore, with machine learning (for the vals, coded by “A,” “B,” and so these discretization methods could sake of correctness, it is not clear on. The authors are probably con- be used in other systems, (in C4.5 what the authors mean by “the fused by the fact that in some or CART). On the other hand, all number of possible instances.” 108 April 2000/Vol. 43, No. 4 COMMUNICATIONS OF THE ACM Most likely they refer to the num- well-established calculus of uncer- the same phenomena. However, in ber of possible different cases tainty. For a long time there was a rough set theory the basic tools are (rows) of the table. They are mis- dispute (and still is) between the sets: lower and upper approxima- taken. The correct number is objective approach to the defini- tions of the concept. These sets 2010^10 = 1.024*10^13). In all tion of probability (based on are well defined and are computed of these areas we may deal with experiments and relative frequen- directly from the input data. big data sets and with potentially cies of outcomes) and the subjec- Thus, rough set theory is objec- large number of different data tive approach (based on experts’ tive, but it does not mean that it is sets. Again, the problem is com- opinions). For example, an indi- superior (or inferior). For exam- mon to all of these disciplines and vidual may observe a game based ple, if input data were pre- by no means occurs just in rough on a random process and evaluate processed and numerical attributes set theory. Fortunately, rough set probabilities. This is the objective were discretized by an expert, the theory offers algorithms with approach. Or, the individual may resulting data might be subjective. polynomial time complexity and ask a gambler how he or she will But again, this preprocessing is space complexity with respect to bet his or her own money. This is not a part of rough set theory, as the number of attributes and the the subjective approach. Cur- we explained previously. Input number of cases. rently, subjectivism prevails in data must be given to initiate Finally, regarding the authors’ probability theory. The propo- rough set theory procedures, and, comments about objectivity of nents of the subjective approach when rough set theory comes into rough set theory, we are puzzled do not show any inferiority com- the picture, its methods are objec- why they assume objectivity plex. The problem is definitely tive with respect to given data. c means superiority. They confuse not which approach is superior. Jerzy W. Grzymala-Busse daily life with science.

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