Chapter: Fundamentals of Database Systems - Advanced Database Models, Systems, and Applications - Data Mining Concepts

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Association Rules

1. Market-Basket Model, Support, and Confidence 2. Apriori Algorithm 3. Sampling Algorithm 4. Frequent-Pattern (FP) Tree and FP-Growth Algorithm 5. Partition Algorithm 6. Other Types of Association Rules 7. Additional Considerations for Association Rules

Association Rules

 

1. Market-Basket Model, Support, and Confidence

One of the major technologies in data mining involves the discovery of association rules. The database is regarded as a collection of transactions, each involving a set of items. A common example is that of market-basket data. Here the market basket corresponds to the sets of items a consumer buys in a supermarket during one visit. Consider four such transactions in a random sample shown in Figure 28.1.

 

An association rule is of the form X => Y, where X = {x1, x2, ..., xn}, and Y = {y1, y2,

..., ym} are sets of items, with xi and yj being distinct items for all i and all j. This association states that if a customer buys X, he or she is also likely to buy Y. In general, any association rule has the form LHS (left-hand side) => RHS (right-hand side), where LHS and RHS are sets of items. The set LHS RHS is called an itemset, the set of items purchased by customers. For an association rule to be of interest to a data miner, the rule should satisfy some interest measure. Two common interest measures are support and confidence.

 

The support for a rule LHS => RHS is with respect to the itemset; it refers to how frequently a specific itemset occurs in the database. That is, the support is the per-centage of transactions that contain all of the items in the itemset LHS RHS. If the support is low, it implies that there is no overwhelming evidence that items in LHS RHS occur together because the itemset occurs in only a small fraction of trans-actions. Another term for support is prevalence of the rule.

 

The confidence is with regard to the implication shown in the rule. The confidence of the rule LHS => RHS is computed as the support(LHS RHS)/support(LHS). We can think of it as the probability that the items in RHS will be purchased given that the items in LHS are purchased by a customer. Another term for confidence is strength of the rule.

 

As an example of support and confidence, consider the following two rules: milk => juice and bread => juice. Looking at our four sample transactions in Figure 28.1, we see that the support of {milk, juice} is 50 percent and the support of {bread, juice} is only 25 percent. The confidence of milk => juice is 66.7 percent (meaning that, of three transactions in which milk occurs, two contain juice) and the confidence of bread => juice is 50 percent (meaning that one of two transactions containing bread also contains juice).

 

As we can see, support and confidence do not necessarily go hand in hand. The goal of mining association rules, then, is to generate all possible rules that exceed some minimum user-specified support and confidence thresholds. The problem is thus decomposed into two subproblems:

 

1. Generate all itemsets that have a support that exceeds the threshold. These sets of items are called large (or frequent) itemsets. Note that large here means large support.


2. For each large itemset, all the rules that have a minimum confidence are generated as follows: For a large itemset X and Y X, let Z = XY; then if sup-port(X)/support(Z) > minimum confidence, the rule Z => Y (that is, XY => Y) is a valid rule.

 

Generating rules by using all large itemsets and their supports is relatively straight-forward. However, discovering all large itemsets together with the value for their support is a major problem if the cardinality of the set of items is very high. A typical supermarket has thousands of items. The number of distinct itemsets is 2m, where m is the number of items, and counting support for all possible itemsets becomes very computation intensive. To reduce the combinatorial search space, algorithms for finding association rules utilize the following properties:

 

        1. A subset of a large itemset must also be large (that is, each subset of a large itemset exceeds the minimum required support).

 

        2. Conversely, a superset of a small itemset is also small (implying that it does not have enough support).

 

The first property is referred to as downward closure. The second property, called the antimonotonicity property, helps to reduce the search space of possible solutions. That is, once an itemset is found to be small (not a large itemset), then any extension to that itemset, formed by adding one or more items to the set, will also yield a small itemset.

 

2. Apriori Algorithm

 

The first algorithm to use the downward closure and antimontonicity properties was the Apriori algorithm, shown as Algorithm 28.1.

 

We illustrate Algorithm 28.1 using the transaction data in Figure 28.1 using a mini-mum support of 0.5. The candidate 1-itemsets are {milk, bread, juice, cookies, eggs, coffee} and their respective supports are 0.75, 0.5, 0.5, 0.5, 0.25, and 0.25. The first four items qualify for L1 since each support is greater than or equal to 0.5. In the first iteration of the repeat-loop, we extend the frequent 1-itemsets to create the candi-date frequent 2-itemsets, C2. C2 contains {milk, bread}, {milk, juice}, {bread, juice}, {milk, cookies}, {bread, cookies}, and {juice, cookies}. Notice, for example, that {milk, eggs} does not appear in C2 since {eggs} is small (by the antimonotonicity property) and does not appear in L1. The supports for the six sets contained in C2 are 0.25, 0.5, 0.25, 0.25, 0.5, and 0.25 and are computed by scanning the set of trans-actions. Only the second 2-itemset {milk, juice} and the fifth 2-itemset {bread, cookies} have support greater than or equal to 0.5. These two 2-itemsets form the frequent 2-itemsets, L2.

 

Algorithm 28.1. Apriori Algorithm for Finding Frequent (Large) Itemsets

 

Input: Database of m transactions, D, and a minimum support, mins, represented as a fraction of m.

Output: Frequent itemsets, L1, L2, ..., Lk

 

Begin                       /* steps or statements are numbered for better readability */

 

        Compute support(ij) = count(ij)/m for each individual item, i1, i2, ..., in by scanning the database once and counting the number of transactions that item ij appears in (that is, count(ij));

 

        The candidate frequent 1-itemset, C1, will be the set of items i1, i2, ..., in;

 

        The subset of items containing ij from C1 where support(ij) >= mins becomes the frequent

 

1-itemset, L1;

 

        k = 1;

 

termination = false; repeat

        Lk+1 = ;

        Create the candidate frequent (k+1)-itemset, Ck+1, by combining members of Lk that have k–1 items in common (this forms candidate frequent (k+1)-itemsets by selectively extending frequent k-itemsets by one item);

 

        In addition, only consider as elements of Ck+1 those k+1 items such that every subset of size k appears in Lk;

        Scan the database once and compute the support for each member of Ck+1; if the support for a member of Ck+1 >= mins then add that member to Lk+1;

 

        If Lk+1 is empty then termination = true else k = k + 1;

 

until termination;

 

End;

 

In the next iteration of the repeat-loop, we construct candidate frequent 3-itemsets by adding additional items to sets in L2. However, for no extension of itemsets in L2 will all 2-item subsets be contained in L2. For example, consider {milk, juice, bread}; the 2-itemset {milk, bread} is not in L2, hence {milk, juice, bread} cannot be a frequent 3-itemset by the downward closure property. At this point the algorithm terminates with L1 equal to {{milk}, {bread}, {juice}, {cookies}} and L2 equal to {{milk, juice}, {bread, cookies}}.

 

Several other algorithms have been proposed to mine association rules. They vary mainly in terms of how the candidate itemsets are generated, and how the supports for the candidate itemsets are counted. Some algorithms use such data structures as bitmaps and hashtrees to keep information about itemsets. Several algorithms have been proposed that use multiple scans of the database because the potential number of itemsets, 2m, can be too large to set up counters during a single scan. We will examine three improved algorithms (compared to the Apriori algorithm) for association rule mining: the Sampling algorithm, the Frequent-Pattern Tree algorithm, and the Partition algorithm.

 

3. Sampling Algorithm

 

The main idea for the Sampling algorithm is to select a small sample, one that fits in main memory, of the database of transactions and to determine the frequent itemsets from that sample. If those frequent itemsets form a superset of the frequent itemsets for the entire database, then we can determine the real frequent itemsets by scanning the remainder of the database in order to compute the exact support values for the superset itemsets. A superset of the frequent itemsets can usually be found from the sample by using, for example, the Apriori algorithm, with a lowered minimum support.

 

In some rare cases, some frequent itemsets may be missed and a second scan of the database is needed. To decide whether any frequent itemsets have been missed, the concept of the negative border is used. The negative border with respect to a frequent itemset, S, and set of items, I, is the minimal itemsets contained in PowerSet(I) and not in S. The basic idea is that the negative border of a set of frequent itemsets contains the closest itemsets that could also be frequent. Consider the case where a set X is not contained in the frequent itemsets. If all subsets of X are contained in the set of frequent itemsets, then X would be in the negative border.

 

We illustrate this with the following example. Consider the set of items I = {A, B, C, D, E} and let the combined frequent itemsets of size 1 to 3 be S = {{A}, {B}, {C}, {D}, {AB}, {AC}, {BC}, {AD}, {CD}, {ABC}}. The negative border is {{E}, {BD}, {ACD}}. The set {E} is the only 1-itemset not contained in S, {BD} is the only 2-itemset not in S but whose 1-itemset subsets are, and {ACD} is the only 3-itemset whose 2-itemset subsets are all in S. The negative border is important since it is necessary to determine the support for those itemsets in the negative border to ensure that no large itemsets are missed from analyzing the sample data.

 

Support for the negative border is determined when the remainder of the database is scanned. If we find that an itemset, X, in the negative border belongs in the set of all frequent itemsets, then there is a potential for a superset of X to also be frequent. If this happens, then a second pass over the database is needed to make sure that all frequent itemsets are found.

 

4. Frequent-Pattern (FP) Tree and FP-Growth Algorithm

 

The Frequent-Pattern Tree (FP-tree) is motivated by the fact that Apriori-based algorithms may generate and test a very large number of candidate itemsets. For example, with 1000 frequent 1-itemsets, the Apriori algorithm would have to generate

 

 

or 499,500 candidate 2-itemsets. The FP-Growth algorithm is one approach that eliminates the generation of a large number of candidate itemsets.

The algorithm first produces a compressed version of the database in terms of an FP-tree (frequent-pattern tree). The FP-tree stores relevant itemset information and allows for the efficient discovery of frequent itemsets. The actual mining process adopts a divide-and-conquer strategy where the mining process is decomposed into a set of smaller tasks that each operates on a conditional FP-tree, a subset (projection) of the original tree. To start with, we examine how the FP-tree is constructed. The database is first scanned and the frequent 1-itemsets along with their support are computed. With this algorithm, the support is the count of transactions containing the item rather than the fraction of transactions containing the item. The frequent 1-itemsets are then sorted in nonincreasing order of their support. Next, the root of the FP-tree is created with a NULL label. The database is scanned a second time and for each transaction T in the database, the frequent 1-itemsets in T are placed in order as was done with the frequent 1-itemsets. We can designate this sorted list for T as consisting of a first item, the head, and the remaining items, the tail. The itemset information (head, tail) is inserted into the FP-tree recursively, starting at the root node, as follows:

 

        1. If the current node, N, of the FP-tree has a child with an item name = head, then increment the count associated with node N by 1, else create a new node, N, with a count of 1, link N to its parent and link N with the item header table (used for efficient tree traversal).

 

        2. If the tail is nonempty, then repeat step (1) using as the sorted list only the tail, that is, the old head is removed and the new head is the first item from the tail and the remaining items become the new tail.

 

The item header table, created during the process of building the FP-tree, contains three fields per entry for each frequent item: item identifier, support count, and node link. The item identifier and support count are self-explanatory. The node link is a pointer to an occurrence of that item in the FP-tree. Since multiple occurrences of a single item may appear in the FP-tree, these items are linked together as a list where the start of the list is pointed to by the node link in the item header table. We illustrate the building of the FP-tree using the transaction data in Figure 28.1. Let us use a minimum support of 2. One pass over the four transactions yields the following frequent 1-itemsets with associated support: {{(milk, 3)}, {(bread, 2)}, {(cookies, 2)}, {(juice, 2)}}. The database is scanned a second time and each transaction will be processed again.

 

For the first transaction, we create the sorted list, T = {milk, bread, cookies, juice}. The items in T are the frequent 1-itemsets from the first transaction. The items are ordered based on the nonincreasing ordering of the count of the 1-itemsets found in pass 1 (that is, milk first, bread second, and so on). We create a NULL root node for the FP-tree and insert milk as a child of the root, bread as a child of milk, cookies as a child of bread, and juice as a child of cookies. We adjust the entries for the frequent items in the item header table.

 

For the second transaction, we have the sorted list {milk, juice}. Starting at the root, we see that a child node with label milk exists, so we move to that node and update


its count (to account for the second transaction that contains milk). We see that there is no child of the current node with label juice, so we create a new node with label juice. The item header table is adjusted.

 

The third transaction only has 1-frequent item, {milk}. Again, starting at the root, we see that the node with label milk exists, so we move to that node, increment its count, and adjust the item header table. The final transaction contains frequent items, {bread, cookies}. At the root node, we see that a child with label bread does not exist. Thus, we create a new child of the root, initialize its counter, and then insert cookies as a child of this node and initialize its count. After the item header table is updated, we end up with the FP-tree and item header table as shown in Figure 28.2. If we examine this FP-tree, we see that it indeed represents the original transactions in a compressed format (that is, only showing the items from each transaction that are large 1-itemsets).

 

Algorithm 28.2 is used for mining the FP-tree for frequent patterns. With the FP-tree, it is possible to find all frequent patterns that contain a given frequent item by starting from the item header table for that item and traversing the node links in the FP-tree. The algorithm starts with a frequent 1-itemset (suffix pattern) and con-structs its conditional pattern base and then its conditional FP-tree. The conditional pattern base is made up of a set of prefix paths, that is, where the frequent item is a suffix. For example, if we consider the item juice, we see from Figure 28.2 that there are two paths in the FP-tree that end with juice: (milk, bread, cookies, juice) and (milk, juice). The two associated prefix paths are (milk, bread, cookies) and (milk). The conditional FP-tree is constructed from the patterns in the conditional pattern base. The mining is recursively performed on this FP-tree. The frequent patterns are formed by concatenating the suffix pattern with the frequent patterns produced from a conditional FP-tree.

Algorithm 28.2. FP-Growth Algorithm for Finding Frequent Itemsets

 

Input: FP-tree and a minimum support, mins

 

Output: frequent patterns (itemsets)

 

procedure FP-growth (tree, alpha);

 

Begin

if tree contains a single path P then

 

for each combination, beta, of the nodes in the path generate pattern (beta alpha)

with support = minimum support of nodes in beta

else

 

for each item, i, in the header of the tree do begin

generate pattern beta = (i alpha) with support = i.support; construct beta’s conditional pattern base;

 

construct beta’s conditional FP-tree, beta_tree; if beta_tree is not empty then

FP-growth(beta_tree, beta);

end;

End;

 

We illustrate the algorithm using the data in Figure 28.1 and the tree in Figure 28.2. The procedure FP-growth is called with the two parameters: the original FP-tree and NULL for the variable alpha. Since the original FP-tree has more than a single path, we execute the else part of the first if statement. We start with the frequent item, juice. We will examine the frequent items in order of lowest support (that is, from the last entry in the table to the first). The variable beta is set to juice with support equal to 2.

 

Following the node link in the item header table, we construct the conditional pat-tern base consisting of two paths (with juice as suffix). These are (milk, bread, cookies: 1) and (milk: 1). The conditional FP-tree consists of only a single node, milk: 2. This is due to a support of only 1 for node bread and cookies, which is below the minimal support of 2. The algorithm is called recursively with an FP-tree of only a single node (that is, milk: 2) and a beta value of juice. Since this FP-tree only has one path, all combinations of beta and nodes in the path are generated—that is, {milk, juice}—with support of 2.

 

Next, the frequent item, cookies, is used. The variable beta is set to cookies with sup-port = 2. Following the node link in the item header table, we construct the conditional pattern base consisting of two paths. These are (milk, bread: 1) and (bread: 1). The conditional FP-tree is only a single node, bread: 2. The algorithm is called recursively with an FP-tree of only a single node (that is, bread: 2) and a beta value of cookies. Since this FP-tree only has one path, all combinations of beta and nodes in the path are generated, that is, {bread, cookies} with support of 2. The frequent item, bread, is considered next. The variable beta is set to bread with support = 2. Following the node link in the item header table, we construct the conditional pattern base consisting of one path, which is (milk: 1). The conditional FP-tree is empty since the count is less than the minimum support. Since the conditional FP-tree is empty, no frequent patterns will be generated.

 

The last frequent item to consider is milk. This is the top item in the item header table and as such has an empty conditional pattern base and empty conditional FP-tree. As a result, no frequent patterns are added. The result of executing the algorithm is the following frequent patterns (or itemsets) with their support: {{milk: 3}, {bread: 2}, {cookies: 2}, {juice: 2}, {milk, juice: 2}, {bread, cookies: 2}}.

 

5. Partition Algorithm

 

Another algorithm, called the Partition algorithm, is summarized below. If we are given a database with a small number of potential large itemsets, say, a few thou-sand, then the support for all of them can be tested in one scan by using a partitioning technique. Partitioning divides the database into nonoverlapping subsets; these are individually considered as separate databases and all large itemsets for that partition, called local frequent itemsets, are generated in one pass. The Apriori algorithm can then be used efficiently on each partition if it fits entirely in main memory. Partitions are chosen in such a way that each partition can be accommodated in main memory. As such, a partition is read only once in each pass. The only caveat with the partition method is that the minimum support used for each partition has a slightly different meaning from the original value. The minimum support is based on the size of the partition rather than the size of the database for determining local frequent (large) itemsets. The actual support threshold value is the same as given earlier, but the support is computed only for a partition.

 

At the end of pass one, we take the union of all frequent itemsets from each partition. This forms the global candidate frequent itemsets for the entire database. When these lists are merged, they may contain some false positives. That is, some of the itemsets that are frequent (large) in one partition may not qualify in several other partitions and hence may not exceed the minimum support when the original database is considered. Note that there are no false negatives; no large itemsets will be missed. The global candidate large itemsets identified in pass one are verified in pass two; that is, their actual support is measured for the entire database. At the end of phase two, all global large itemsets are identified. The Partition algorithm lends itself naturally to a parallel or distributed implementation for better efficiency. Further improvements to this algorithm have been suggested.

 

6. Other Types of Association Rules

 

Association Rules among Hierarchies. There are certain types of associations that are particularly interesting for a special reason. These associations occur among hierarchies of items. Typically, it is possible to divide items among disjoint hierarchies based on the nature of the domain. For example, foods in a supermarket, items in a department store, or articles in a sports shop can be categorized into classes and subclasses that give rise to hierarchies. Consider Figure 28.3, which shows the taxon-omy of items in a supermarket. The figure shows two hierarchies—beverages and desserts, respectively. The entire groups may not produce associations of the form beverages => desserts, or desserts => beverages. However, associations of the type Healthy-brand frozen yogurt => bottled water, or Rich cream-brand ice cream => wine cooler may produce enough confidence and support to be valid association rules of interest.

 

Therefore, if the application area has a natural classification of the itemsets into hierarchies, discovering associations within the hierarchies is of no particular inter-est. The ones of specific interest are associations across hierarchies. They may occur among item groupings at different levels.

 

Multidimensional Associations. Discovering association rules involves searching for patterns in a file. In Figure 28.1, we have an example of a file of customer transactions with three dimensions: Transaction_id, Time, and Items_bought. However, our data mining tasks and algorithms introduced up to this point only involve one dimension: Items_bought. The following rule is an example of including the label of the single dimension: Items_bought(milk) => Items_bought(juice). It may be of interest to find association rules that involve multiple dimensions, for


example, Time(6:30...8:00) => Items_bought(milk). Rules like these are called multidimensional association rules. The dimensions represent attributes of records of a file or, in terms of relations, columns of rows of a relation, and can be categorical or quantitative. Categorical attributes have a finite set of values that display no ordering relationship. Quantitative attributes are numeric and their values display an ordering relationship, for example, <. Items_bought is an example of a categorical attribute and Transaction_id and Time are quantitative.

 

One approach to handling a quantitative attribute is to partition its values into nonoverlapping intervals that are assigned labels. This can be done in a static man-ner based on domain-specific knowledge. For example, a concept hierarchy may group values for Salary into three distinct classes: low income (0 < Salary < 29,999), middle income (30,000 < Salary < 74,999), and high income (Salary > 75,000). From here, the typical Apriori-type algorithm or one of its variants can be used for the rule mining since the quantitative attributes now look like categorical attributes. Another approach to partitioning is to group attribute values based on data distribution, for example, equidepth partitioning, and to assign integer values to each partition. The partitioning at this stage may be relatively fine, that is, a larger number of intervals. Then during the mining process, these partitions may combine with other adjacent partitions if their support is less than some predefined maxi-mum value. An Apriori-type algorithm can be used here as well for the data mining.

 

Negative Associations. The problem of discovering a negative association is harder than that of discovering a positive association. A negative association is of the following type: 60 percent of customers who buy potato chips do not buy bottled water. (Here, the 60 percent refers to the confidence for the negative association rule.) In a database with 10,000 items, there are 210,000 possible combinations of items, a majority of which do not appear even once in the database. If the absence of a certain item combination is taken to mean a negative association, then we potentially have millions and millions of negative association rules with RHSs that are of no interest at all. The problem, then, is to find only interesting negative rules. In general, we are interested in cases in which two specific sets of items appear very rarely in the same transaction. This poses two problems.

 

       1. For a total item inventory of 10,000 items, the probability of any two being bought together is (1/10,000) * (1/10,000) = 10–8. If we find the actual sup-port for these two occurring together to be zero, that does not represent a significant departure from expectation and hence is not an interesting (negative) association.

 

2. The other problem is more serious. We are looking for item combinations with very low support, and there are millions and millions with low or even zero support. For example, a data set of 10 million transactions has most of the 2.5 billion pairwise combinations of 10,000 items missing. This would generate billions of useless rules.


Therefore, to make negative association rules interesting, we must use prior knowledge about the itemsets. One approach is to use hierarchies. Suppose we use the hierarchies of soft drinks and chips shown in Figure 28.4.


A strong positive association has been shown between soft drinks and chips. If we find a large support for the fact that when customers buy Days chips they predominantly buy Topsy and not Joke and not Wakeup, that would be interesting because we would normally expect that if there is a strong association between Days and Topsy, there should also be such a strong association between Days and Joke or Days and Wakeup.

 

In the frozen yogurt and bottled water groupings shown in Figure 28.3, suppose the Reduce versus Healthy-brand division is 80–20 and the Plain and Clear brands division is 60–40 among respective categories. This would give a joint probability of Reduce frozen yogurt being purchased with Plain bottled water as 48 percent among the transactions containing a frozen yogurt and bottled water. If this sup-port, however, is found to be only 20 percent, it would indicate a significant negative association among Reduce yogurt and Plain bottled water; again, that would be interesting.

 

The problem of finding negative association is important in the above situations given the domain knowledge in the form of item generalization hierarchies (that is, the beverage given and desserts hierarchies shown in Figure 28.3), the existing positive associations (such as between the frozen yogurt and bottled water groups), and the distribution of items (such as the name brands within related groups). The scope of discovery of negative associations is limited in terms of knowing the item hierarchies and distributions. Exponential growth of negative associations remains a challenge.

 

7. Additional Considerations for Association Rules

 

Mining association rules in real-life databases is complicated by the following fac-tors:

 

        The cardinality of itemsets in most situations is extremely large, and the volume of transactions is very high as well. Some operational databases in retailing and communication industries collect tens of millions of transactions per day.

 

1. Transactions show variability in such factors as geographic location and sea-sons, making sampling difficult. 

2. Item classifications exist along multiple dimensions. Hence, driving the discovery process with domain knowledge, particularly for negative rules, is extremely difficult.

3.Quality of data is variable; significant problems exist with missing, erroneous, conflicting, as well as redundant data in many industries.



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