Not only are the outcomes inconclusive, they are radically different. This highlights another limitation of the method, in that all five measures are treated equally, even though State probably has nothing to do with outcome, while experience may be critical. The coincidence matrix for this application using the four conclusive predictions is shown in Table 3.
This result was terrible. It did not correctly classify any cases. The actually excellent applicant was classified as unacceptable. The actually Table 3. This is the reverse of what a model should do. We can go ahead and demonstrate the application of the method to a new case.
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Here there clearly is a stronger match with record 2 than with any other record. However, there are some negative aspects demonstrated in this case. The applicant is quite a bit older than any case in the training set. Training sets should be as comprehensive as possible relative to the cases they are used to classify.
Weighting provides another means to emphasize certain variables over others. For instance, in this set of data, we could apply the weights shown in Table 3. Matching weights Record Weight 1 2 3 4 5 6 7 8 9 10 Age 0. But spurious matches, such as with age or state, will have much less influence on the outcome. Distance Minimization The next concept uses the distance measured from the observation to be classified to each of the observations in the known data set.
Job applicant data: In this case, the nominal and ordinal data needs to be converted to meaningful ratio data. Categorical data such as age group now must represent distance, where the distance from a given observation to each group indicates equal distance. For instance, the distance to age below 30 should be the same as the distance to the interval between 30 and The best way to treat the age variable is to return to the original values of applicant age. If we attempt to measure the distance from the ten records in the memory database, we immediately encounter the problem of scale.
Age is measured in terms ranging from 22 to 33 years, while state and degree are either 0 or 1, a range of 1. Experience ranges from 0 to 5 years. We might think that some of these variables are more important than others in predicting outcome, but initially we have no reason to expect any difference, and to arbitrarily overweight age because of the scale it is measured on is unattractive. Therefore, we want to normalize scales.
One way to do that is to subtract the minimum from each observation and divide by the range. This, however, can result in complications if applicants are older than the maximum in the data set. There are reasons to Distance Minimization 45 want a training database including all possible values for each variable, but even if this is possible, future observations might spill over maxima or under minima. So we might normalize by assigning the expected minima and maxima for each variable. The maximum age considered is 50, and the minimum Ages of 20 and below are assigned a value of 0, ages of 50 and above a value of 1.
For instance, the oldest applicant expected might be 50 years old, and the youngest For example, the database here is dominated by Californians. If being Californian were considered a positive social characteristic, one could assign the value CA a 1, and all others a 0. Degree is ordinal. This variable would be problematic if there were more than two degrees. For instance, if non-degree applicants as well as Ph. Ds were included, the assignment of 0 for no degree, 1 for B.
The first problem that arises is that this implied an equal distance between degrees. Possibly the distance between a Ph. Furthermore, for this application there is no guarantee that the correct valuation might not be 0 for Ph. In the specific case of this data set, there are fortunately only two entries. Therefore, we can assign 0 for Certification and B. This does not need to imply any order or value. The same problem arises in assigning value to major. In this case, we assume that the ideal major would be information systems, although engineering, computer science, and science would also be useful backgrounds.
For this specific job, business administration would be useful but not exactly what the job focused upon. The variable major is transformed assigning information systems a value of 1. The last input variable in the data set was years of experience. This was originally a continuous number, and we can return to that data form. After 5 years, all of the experience needed is considered to be obtained, so experience of 5 years or more is assigned a value of 1. Coded values are given in Table 3. Past job applicants — Coded Record 1 2 3 4 5 6 7 8 9 10 Age 0. This distance can be measured a number of ways.
The most commonly used are absolute value and squared value. This distance comes to 1. Coded test data for job applicants Record 11 12 13 14 15 Age 0. In all three measures, the closest fit was with record 3, which had an Adequate outcome. Thus, test record 11 would be assigned an outcome of Adequate by all three measures. Results for this method for all five test cases are shown in Table 3.
The coincidence matrix for the absolute value distance metric is shown in Table 3. Distance measures from record 11 to training data Training record 1 2 3 4 5 6 7 8 9 10 Absolute value 1. Coincidence matrix for absolute value distance metric Actual Unacceptable Minimal Adequate Excellent Total Unacceptable 0 0 0 1 1 Minimal 0 0 0 0 0 Adequate 1 2 0 0 3 Excellent 0 0 1 0 1 Inconclusive 0 0 0 0 0 Total 1 2 1 1 5 48 3 Memory-Based Reasoning Methods The results of this very small sample are very bad, in that not one correct classification of the test data was obtained.
In fact, the one applicant that turned out to be excellent was rated as unacceptable. The squared distance metric produced the same five outcomes. The minimization of maximum distance often called the Tchebycheff metric gave the same outcomes as the other distance metrics except for the three cases where it was inconclusive. The Tchebycheff metric suffers from a tendency to have many ties in distance calculations over standardized data because of the propensity for a distance of 1. Keep in mind that this is a very small sample, and the method could improve a great deal with more samples.
However, one major flaw in distance methods is that they are adversely affected by spurious variables, which actually have no relationship to the issue at hand. Not being from California in this case could have a major impact, especially in a small sample as used to demonstrate the method here. The first new applicant was a 26 year old Californian with a B.
The applicant is closest to record 7, which had an adequate outcome. A 35 year old applicant from Ohio with a Masters degree in computer science and 12 years experience would be coded: Table 3. Distance can be measured a lot of different ways. The most commonly used measure is squared distance, which gives greater emphasis to great distances than to small distances.
The distance measure is simply the sum of squares of differences between the record value and the new value over all measures. In this case the test data yielded the same result as the absolute value calculation. This often happens, but the squared distance is more affected by extreme differences.
That could be a good thing if the measures were accurate, and if the variables included were important. However, in this case, being from a different state would not be expected to be important, but would affect the squared distance calculation more than the absolute value calculation. In this sense, the absolute value calculation is considered more robust. There is a third commonly used distance calculation, to minimize the maximum difference. This would be equivalent to taking the maximum absolute value difference among all of the variables in each group, and selecting that group with the minimum of these maximum absolute values.
This distance metric is the one that is most affected by extreme values. This is also referred to as the nearest-neighbor method. While such methods often do well in many problems, they have the deficiency of not having generalizable rules. Predictions require processing the entire body of available historic data. Summary Memory-based reasoning was proposed by Waltz and Kasif as an important framework for general intelligent applications, with the ability to include probabilistic aspects of problems. Memory-based reasoning methods are appropriate in domains with a need for very fast response to rapidly changing environments.
The attractive feature of memory-based reasoning methods is their simplicity. This is counterbalanced by their computational and computer storage complexity. It is difficult to find too many software packages supporting this type of analysis. However, association rules, which can be identified through memory-based reasoning, are often the basis of text mining software.
There are some data features that were identified in our examples. One way to adjust to this data reality is to assure that the training and test cases include all outcome categories. Further, interpretation of output should consider the proportion of outcomes. If there are too many matches tied for best, it will help to transform the continuous variables into more groups or divide categorical variables into more categories.
This will provide additional discriminatory power. At the other extreme, of course, too many groups may lead to no matches. It will often be necessary to calibrate this feature. Whiting Oracle boosts data analysis, Informationweek , 14 June, Waltz, S. Kasif Table 3A. State is not expected to have a specific order prior to analysis, nor is major. Association rule methods are an initial data exploration approach that is often applied to extremely large data set. An example is grocery store market basket data. They have been applied to a variety of fields, to include medicine3 and medical insurance fraud detection.
Agrawal and Srikant presented the a priori algorithm that has been 1 2 3 4 5 6 Y.
Chen, K. Tang, R. Shen, Y. Hu Market basket analysis in a multiple store environment, Decision Support Systems , — Lucchese, S. Orlando, R. Perego Souliou, A. Pagourtzis, N.
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Drosinos, P. Tsanakas Exarchos, C. Papaloukas, D. Fotiadis, L. Michalis Tsoi, S. Zhang, M. Hagenbuchner Buddhakulsomsiri, Y. Siradeghyan, A. Zakarian, X. Li Association rule-generation algorithm for mining automotive warranty data, International Journal of Production Research , — Agrawal, T. Izmielinski, A. Swami Han, J. Pei, Y. Yin Most, such as the APriori algorithm identify correlations among transactions consisting of categorical attributes using binary values.
Some data mining approaches involve weighted association rules for binary values,10 or time intervals. Data structure is an important issue due to the scale of data usually encountered. Imielinksi and Mannila suggested a short-term research program focusing on efficient algorithms and more intelligent indexing techniques, as well as a long-term research program15 to increase programmer productivity through more efficient tools to aid knowledge discovery. Lopes et al. Agrawal, R. Srikant Hipp, U. Nakhaeizadeh Vaitchev, R. Missaoui, R.
Godin, M. Meridji Cai, W. Fu, C. Cheng, W. Kwong Mining association rules with weighted items, Proceedings of International Database Engineering and Applications Symposium, Cardiff, Wales, 68— Lu, H. Hu, F. Mining weighted association rules, Intelligent Data Analysis, 5, — Mild, T. Reutterer Decker, K. Monien Yan, S. Zhang, C. Zhangt On data structures for association rule discovery, Applied Artificial Intelligence , 57— Lee, J. Liu Imielinski, H. Mannila A database perspective on knowledge discovery, Communications of the ACM , 58— Market-Basket Analysis 55 in inference problems.
Grossman et al. Support refers to the degree to which a relationship appears in the data. Confidence relates to the probability that if a precedent occurs, a consequence will occur. Market-Basket Analysis Market-basket analysis refers to methodologies studying the composition of a shopping basket of products purchased during a single shopping event. This technique has been widely applied to grocery store operations as well as other retailing operations, to include restaurants.
Market basket data in its rawest form would be the transactional list of purchases by customer, indicating only the items purchased together with their prices. This data is challenging because of a number of characteristics x A very large number of records often millions of transactions per day x Sparseness each market basket contains only a small portion of items carried x Heterogeneity those with different tastes tend to purchase a specific subset of items.
The aim of market-basket analysis is to identify what products tend to be purchased together. Analyzing transaction-level data can identify 17 S. Lopes, J. Petit, L. Lakhal Grossman, M. Hornick, G. Meyer Data mining standards initiatives, Communications of the ACM , 59— Apte, B. Liu, E. Pednault, P. Smyth Business applications of data mining, Communications of the ACM 49— This information can be used in determining where to place products in the store, as well as aid inventory management.
Product presentations and staffing can be more intelligently planned for specific times of day, days of the week, or holidays. Another commercial application is electronic couponing, tailoring coupon face value and distribution timing using information obtained from marketbaskets.
Experiments with real life datasets on some of the methods such as loose-coupling through a SQL cursor interface, encapsulation of a mining algorithm in a stored procedure, caching the data to a file system on-the-fly and mining, tight-coupling using primarily userdefined functions, and SQL implementations for processing in the DBMS have been conducted and compared. The stores can use this information by putting these products in close proximity of each other and making them more visible and accessible for customers at the time of shopping.
These assortments can affect customer behavior and promote the sales for complement items. The other use of this information is to decide about the layout of catalogs and put the items with strong association together in sales catalogs. The advantage of using sales data for promotions and 20 G. Russell, A.
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Petersen Analysis of cross category dependence in market basket selection, Journal of Retailing , — Simoudis Reed Sarawagi, S. Thomas, R. Agrawal Market-Basket Analysis 57 store layout is that the consumer behavior information determines the items with associations. This information may vary based on the area and the assortments of available items in stores and the point of sale data reflects the real behavior of the group of customers that frequently shop at the same store.
Catalogs that are designed based on the market basket analysis are expected to be more effective on consumer behavior and sales promotion. Market basket analysis is an undirected method. This method can reveal the associations that may be unknown to the store management.
The products that are most important are revealed through this analysis. The current study provides the method of querying for specific products as well. Market basket analysis can be used to identify the items frequently sold to new customers and profiling the customer baskets during a period of time by identifying customers through membership shopping cards. Demonstration on Small Set of Data Market basket analysis involves large scale datasets. To demonstrate methods, we will generate a prototypical dataset consisting of 10 grocery items, with 25 market baskets.
Table 4. The diagonal contains the total number of market baskets containing each item. It can be seen that most who purchased apples also purchased milk, bread, and cola. Of those that purchased beer, few purchased water. One customer purchased everything, so there are no zeros in this matrix. Realize that this set of data appears to have inflated sales, but it is for purposes of demonstration.
Real groceries would have many more items, with many more zeros. But our data can be used to demonstrate key measures in association rules. Support is the number of cases for a given pair. The support for apples and milk is For beer and water it is one. Minimum support can be used to control the number of association rules obtained, with higher support requirements yielding fewer pairs. A minimum support of 1 would have 45 pairs, the maximum for 10 items.
Minimum support of 15 would have no rules. Note that association rules can go beyond pairs to triplets, sets of four, and so on. But usually pairs are of interest and much easier to identify. For a given pair, confidence is the proportion of true conditions to total possible. Bread and milk had the highest support of 11 cases. Confidence is relative to the base case. Minimum confidence levels can be set in most data mining software, again with a higher level yielding fewer pairs.
Improvement is a measure of how much stronger or weaker a given pair occurrence is relative to random. For instance, there were 15 cases of apples occurring.
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There were a total of 25 market baskets, with 95 total items sold. Random would therefore have any particular market basket containing 3. Of the 15 market baskets containing any given item, there are on average 2. For 15 items then, random expected number of complementary items would be 0. Only beer and water fell below this expectation. A minimum improvement parameter of 2 would require 9. Real Market Basket Data A set of real grocery store data was obtained that we can use to demonstrate the scale of market-basket analysis.
Point of sale data from 18 stores in the area was collected in a central system, with each transaction including the store code. The point of sale data was collected in a flat file and each record included the item identifier UPC code, the store number, date of sale, basket identifier, and other information. The first step was data cleaning because some sales records lacked the basket identifier or the UPC code.
In this step about 4. The eliminated data was for discount coupons, not considered a product. In the second step the text formatted data of transaction records was loaded into a relational database for querying. Customer basket analysis involves finding the number of occurrences of every item to all other items to find possible relationships between them. Two problems in a real world dataset arise. First, there are usually tens of thousands of items in grocery 25 Data obtained from an anonymous store by M.
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Finding efficient algorithms for candidate item generation is a significant part of data mining research. The second problem is that recording the results in commercial database management systems is not possible due to the limits in the number of columns, even though we can save the results in text format. To explain this limitation, we have to look at Fig. Every tuple includes the UPC code of the item and a transaction identifier. One solution for sorting the items that repeat to be in the same transaction together is extracting them as data elements of a tuple, but this tuple may include a large number of elements, with a limitation on the number of columns in most of commercial database systems at , this option is not practical.
A two step approach to screen data records in first step was used to identify the potential important items and made the analysis practical. The structure of sales data table items that were important to this analysis is illustrated in Fig. Candidate generation through a simple SQL query was used to scan every item in the dataset and count the total number of other items that share the same transaction code with that item. One criterion that is important at this step is the minimum support for groups of repetitions.
Minimum support could be implemented in two ways; counting the percentage of transactions, or counting the occurrence of items and then filtering out the counts that do not meet the minimum count. The second way was implemented in this study, setting a minimum number of counts as the minimum support required for candidate items.
Query 1 candidate items table Tc. The SQL query that extracts this set is given in Fig. Results were grouped by UPC codes of items. Sorting the results based on the count of other items that go with each particular item provided a list of items that identify the potential important items and substantially narrowed down the number of candidates for final step of analysis.
A test run of all items for counting the number of all items together showed no difference in results. This step narrowed down a list of about , items to 2, top items that can potentially be important for our study. Another query that counted the number of individual items that had occurrences with each item that was not between these top 2, items showed no significant results. The advantage of this method for candidate generation is simplicity of the method, standard SQL support in all database systems.
As later confirmed in comparison with commercial data mining system results later in this study, this method returns a more complete set of candidates while has the flexibility of adjusting the minimum support indicator minsup The second step after candidate generation was checking each item in the candidate list against each other. There are different alternatives for this step, including stored procedures in database system, and standard application interface to the database system.
Since the purpose of this study is providing a portable solution accessible by all platforms, the standard application interface was the proper choice. Developing this simple interface is easy and practical, following the algorithm in this section. This application provided the output as matrix of frequencies in text format.
The output data was loaded into commercial data mining system PolyAnalyst to find the associations of items together and later comparing them to the final results of our analysis without using the commercial data mining software. PolyAnalyst supports traditional market basket analysis, which treats data as binary in the sense that products are either purchased together yes or not no. It also can utilize additional data, such as product volume or dollar value. In our case, we applied traditional market basket analysis.
The software required three input parameters: 62 4 Association Rules in Knowledge Discovery 1. Minimum support sets the minimum proportion of transactions that should contain a basket of products in order to be considered. If this number is set high, only those products that appear very often will be considered, which will lead to a small number of product clusters with few products. This would be useful if one-to-one rules of important products were desired. Setting a low minimum support level will generate many rules including many products.
Minimum confidence sets the probability that if one item is purchased, another item will be. The higher this value is set, the more discriminating the resulting association rule. However, if minimum confidence is set too high, there is a higher likelihood of no rules being generated. Minimum improvement sets how much better the association rule generated is at predicting the presence of a product in a market basket than would be obtained at random.
Again, higher settings would result in fewer association rules being generated, while lower settings would yield more rules. We used the default setting of 2. PolyAnalyst yielded groups of associated products. These groups contained from 2 to 78 products. While this output can be extremely useful to retail organizations, it requires investment in data mining software and more importantly prioritizing item sets from a large number of sets in output.
The same information could be obtained through relational database software. The first step identifies the items that are sold with larger number of other items. The purpose of this step is narrowing the list of items down for efficiency of calculations and time of computation. In the current study we selected top items, which had minimum support in entire data set. The criterion at this step was having 50 and more combined times of sales with other items in transactions.
We call this the top items list. SQL query each item along with the number of times these two items are sold together. The SQL query Fig. The algorithm is illustrated in Fig.
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In every record, an item in the sales list is in the first column, one frequent pair item is in the second field, and the number of times these two items are sold together is in the third field, and this record is taken for every item. The advantage of this method is that by simply sorting the result based on the number of repetitions we can get the most frequent items that are sold together. In case the store managers want to get the results for any specific item, this table can be easily grouped by UPC code in the first field and sorted for number of repetitions.
We compared database queries with a commercial data mining software, PolyAnalyst. The dataset analyzed with PolyAnalyst software produced 78 groups of products, identified by their UPCs. Query for UPC codes result was to run queries to find out about the accuracy of the output report. The query that checks the results has the syntax given in Fig. The following set of results from one of the output groups is an example of the groups of UPC codes matched by PolyAnalyst. The UPC codes for one of the output groups, which had a relatively high volume for a low number of products 4 , are given below.
This relationship has been identified by the output report of PolyAnalyst. The first row has been identified by the software but not the second row with 18 occurrences, while the software included associations with fewer occurrences in most of the output groups. This relationship is identified by the software. In this case, the relationship has been better identified through database querying. The querying process was repeated for a group including 22 products Group 1.
Some of these items were tested for efficiency of the output report by comparing the set to query results from the database. UPC codes for Group 1 Real Market Basket Data 67 Table 4. On the other hand, some items will have lower matches. Results of querying database for this item are shown in Table 4. Each row of this table is an output of a separate query on the database. Conclusions Association rules are basic data mining tools for initial data exploration usually applied to large data sets, seeking to identify the most common groups of items occurring together.
Book Concept Our intent is to cover the fundamental concepts of data mining, to dem- strate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. We have organized the material into three parts. Part I introduces concepts.
Part II contains chapters on a number of different techniques often used in data mining. Part III focuses on business applications of data mining. Not all of these chapters need to be covered, and their sequence could be varied at instructor design. The book will include short vignettes of how specific concepts have been applied in real practice. The challenge is how to keep those books in a way that readers can take several books on a particular topic without hassle. By using the clustering technique, we can keep books that have some kinds of similarities in one cluster or one shelf and label it with a meaningful name.
If readers want to grab books in that topic, they would only have to go to that shelf instead of looking for the entire library. For instance , the prediction analysis technique can be used in the sale to predict profit for the future if we consider the sale is an independent variable, profit could be a dependent variable. Then based on the historical sale and profit data, we can draw a fitted regression curve that is used for profit prediction.
Sequential patterns analysis is one of data mining technique that seeks to discover or identify similar patterns, regular events or trends in transaction data over a business period. In sales, with historical transaction data, businesses can identify a set of items that customers buy together different times in a year.
Then businesses can use this information to recommend customers buy it with better deals based on their purchasing frequency in the past. The A decision tree is one of the most commonly used data mining techniques because its model is easy to understand for users. In decision tree technique, the root of the decision tree is a simple question or condition that has multiple answers. Each answer then leads to a set of questions or conditions that help us determine the data so that we can make the final decision based on it.
For example, We use the following decision tree to determine whether or not to play tennis:. Starting at the root node, if the outlook is overcast then we should definitely play tennis. If it is rainy, we should only play tennis if the wind is the week.
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