Shopping Behavior among Hispanics:
A Market Basket Analysis
Final Paper
Introduction
Historically, retailers have placed the majority of their marketing strategies and
advertising on the Anglo-American consumer. Even when businesses finally began to recognize
that minority markets were in fact viable, marketers and retailers treated these ethnic groups in a
similar fashion as Anglo-Americans (Mulhern & Williams, 1994). Successful marketing
campaigns merely had their language and personalities altered for the intended ethnic market that
companies were trying to reach. Marketing strategies aimed at the Hispanic consumer consisted
of little more than translating commercials and print into Spanish. It was a common practice to
simply treat ethnic markets as extensions of the Anglo-American market. The idea was that of
mass marketing, which treated the marketplace as one homogenous group (Shim & Eastlick,
1998).
Today marketers are clearly differentiating ethnic groups by clearly identifiable patterns
in their shopping and purchasing behavior. This effort is the marketers’ response to an everdiverse America. The shift is towards recognition of the importance in changing the retail mix
and marketing strategies to better meet the needs and wants of minority markets. This ethnic
differentiation process is focused primarily on measurable characteristics such as age, sex,
socioeconomic levels, and ethnicity (Mellgren, 2010).
1
The Hispanic-American market has received much attention from marketers in recent
years. With its extensive growth, this ethnic market is making it necessary for companies to offer
an assortment of products and services that will appeal to Hispanic-Americans. The Hispanic
population accounts for approximately 13% of the U.S. population, or just over 35 million
people. The 2010 Census reported a 58% increase in the Hispanic population over the 1990
Census. The numbers indicate that Hispanic-Americans are on the verge of becoming the
nation’s largest minority group, surpassing African-Americans (Chain Store Age, 2006; Rivkin,
2007)).
Also, of major interest to marketers is Hispanic-American’s rising purchasing power.
Hispanic Americans account for $428 billion in spending per year. Nicholls and Roslow (1996)
found that Hispanic-Americans spent more money while shopping, with 43% spending $50 or
more compared to 32.7% of non-Hispanics who spent the same amount. Properly understanding
the differences between the Hispanic-American market and Anglo-American market can be
financially beneficial to businesses. Not only it is critical to understand such as population
percentage, population growth, buying power, all of which make the Hispanic population
attractive to retailers, but it is necessary to understand their shopping patterns that set this ethnic
market apart from the majority consumer group.
Tailoring marketing promotions and strategies can help a company more effectively
reach unique ethnic markets. Hispanic-Americans’ wants and needs, which were previously
overlooked, is not an integral part of the marketing strategy. Understanding shopping behavior of
Hispanic-Americans will help to further understand this group as consumers. Through a better
understanding of this ethnic segment, marketers and retailers will be more capable of meeting
their wants and needs.
2
Hispanic Shopping Behaviors. Although some have noted that Hispanic Americans are changing
in their shopping behaviors (Silent Revolution, p. 7), researchers have noted that HispanicAmerican families tend to spend more of its disposable income on well-known brands and
demonstrate brand loyalty as a consumer group. This ethnic population is particularly sensitive to
products advertised or labeled in Spanish or that include that reference to their culture. A recent
survey by Nielsen provides some specific details on the shopping behaviors of Hispanic
Americans. Nielsen found that there is a preference among Hispanics to stop at stores that
resonate with the sights, sounds, smells, and sensibilities of their homeland. This sense of
nostalgia helps create an important connection with the Hispanic consumer. The report advises
that retailers can create a familiar sense of community and comfort zone for consumers through
product assortment, importing specialty lines and stocking items with bilingual packaging, hiring
bilingual employees, posting bilingual signage and distributing bilingual coupons. Other key
findings include: (a) as Hispanics become more acculturated, there is less evidence of brand
loyalty, (b) Hispanic shopping is a family affair involving multiple family members across a
variety of ages; retailers wanting to attract the attention of the Hispanic consumer would benefit
from creating a family-friendly atmosphere, with such amenities as balloons for children and rest
areas for seniors, (c) there is a certain reverence extended to elders within the Hispanic culture
that should be reflected in staff dealings with older shoppers; and (d) many Hispanic Americans
make a concerted effort to keep in touch with those living in their homelands.
Hispanic Values and Shopping. Most Hispanic-Americans come from highly relational and
collectivistic cultures. Studies have found that in their shopping behaviors, Hispanic-Americans
are partly motivated by these values. For example, Crispell (1997) noted that Hispanics are more
likely than others to have a shopping companion. This suggests a collectivistic approach whereby
3
Hispanic-Americans find in shopping a highly relational experience. Moreover, Crispell (1997)
also found that this ethnic group was found to be “slow” and spend a considerable amount of
time taking their time while shopping. Culturally, this group seems to devote considerable time
to family centered events or similar relational experiences. Shopping would be culturally
desirable to the extent that it fits these cultural preferences (Chain Store Age, 1998).
Nichols et al. (1997), for example, researched five situation dimensions influencing buy
behavior that includes physical surroundings, social surroundings, and temporal perspective.
They found that physical surroundings influence the purchase behavior of Hispanics. In the mall
where this study was conducted, there were four department stores and approximately 200
boutiques and specialty shops. The analysis provides an understanding of specific situations
affecting customers at the time of purchase. The Hispanics who spent a longer time in the mall
purchased food or beverage, bought other products, spent $10 or more, and entered five or more
stores. The study shows a certain coherence in the patterns of Hispanic shopper’ purchase
behaviors and their situational dimensions. They make the trip worthwhile by travelling with
companions, consummating a purchase while at the mall, and buying food or beverage during
their visit. The food or beverage purchased is largely consumed on the premises.
An Example: Market Basket
a) Case Description
Most of the empirical research on Hispanic shopping behavior has attempted to investigate
cross-cultural differences in shopping behavior. Studies have compared such behaviors as brand
loyalty, price sensitivity, advertising response, coupon usage, and national brand preference.
However, there is no systematic study of a specific ethnic group and specifically looking at
retain shopping behavior. Research and analysis on Hispanic purchase behavior can benefit
4
from focused studies that examine shopping patterns. To more effectively target Hispanic
shoppers, retailers need to understand how those consumers respond to retail merchandising
practices.
Market Basket Analysis, also known as association-rule mining, is a method of discovering
purchasing patterns of customers by extracting associations of co-occurrences from
transactional records saved in one or more databases (Decker & Monien, 2003). In other words,
market-basket describes corrections, or interdependence in demand relationships, among items
in the customer’s shopping basket (Harikumar & Nagadevara, 2012). Many applications of
market basket analysis are available in the literature for the obvious reason that information
obtained from the analysis can be used in forming marketing, sales, service, and operation
strategies (Athappilly, Razi, & Tarn, 2010). Dasari and Kurkehakar (2011) cite Adams (2008)
who proposed: “To stay competitive, retailers must embrace an analytical, guided and
prescriptive approach to understand their business better and anticipate customer behavior in a
proactive manner” (p. 7). More specifically, Dasari and Kurkehar (2011) outline the benefits of
using market basket analysis, and what retailers can accomplish: (1) develop more profitable
and advertising promotions, (2) target offers more precisely, (3) improve loyalty card
promotions with longitudinal analysis, (4) attract more traffic into the stores, (4) increase the
size and value of the basket purchases, (5) test and learn by using marketplace as laboratory, (6)
empower planners and merchants to make smarter decisions, (7) determine magic price point
for the individual stores, and (8) match inventory with needs by customizing layouts,
assortment, and pricing to the local demography.
This case applies this robust data mining methodology to a market basket database obtained
from a retail store in Spokane, WA. El Mercado del Pueblo (“The Market of the People”) is a
5
store that attracts a diverse population of shoppers. Centrally located in Spokane and with
proximity to Downtown Spokane, this business provides the data representativeness that is
needed for a valid and reliable analysis of shopping behavior. A random sample of customers (n
= 15) was selected. Random samples provide some robustness to analytic studies because they
allow participants to have an equal opportunity to be part of the study and therefore it removes
some potential bias in the study. The shoppers were all Hispanic-American. This study did not
allow the collection of specific demographic data regarding ancestry (i.e., Mexican American
versus Cuban American). Most business analytic studies have used aggregate data and drawn
broad cultural inferences and findings from Hispanic-Americans as a group. It is recommended
that future studies examine more specifically intracultural differences by comparing the
heterogeneity that exists even within the Hispanic-American community.
b) Objective from the case
This researcher collected this hypothetical random data in September 2012 as part of this
project. From an extensive review of the business and marketing literature, the following
hypothesized correlations and associations were formulated for the Market Basket Analysis:
1. Hispanic-Americans will shop for items that have a high level of cultural meaning. These
items will be associated with cultural practices (i.e. holidays) (Donthu & Cherian, 1994).
2. Hispanic-Americans will demonstrate a preference for items that have labels or
instructions in Spanish.
3. Hispanic-Americans will purchase primarily items that are consumed as a family and
have a high collective meaning (Enis, 2009).
4. Hispanic-Americans will have less of a preference for Americans considered
“Americanized” or strongly associated with the U.S. culture.
6
5. Hispanic-Americans will demonstrate a strong loyalty to commonly known brands in the
U.S. Hispanic Culture (Fisher, 1993).
An extensive review of the retail literature on Hispanic shopping behavior yielded the
following items that are frequently obtained by Hispanic-Americans: (1) milk, (2) bottled tea,
(3) spirits – wine, (4) carbonated drinks, (5) dried vegetables and grains, (6) hair care, (7)
shortening oil, (8) women’s fragrances, (9) mozzarella, (10) queso fresco (fresh cheese), (11)
queso blanco (white cheese), (12) tortilla, (13) vanilla, (14) taco, (15) saffron, (16) tequila, (17)
cilantro, (18) carne asada (marinated meat), (19) corona (Mexican beer), (20) maize (corn
grown in Latin America), (21) juguete (toys marketed in Spanish), (22) musica (Hispanic
folkloric music), and (23) pelicula (movies with Hispanic American content). This select
number of items was culled from shopping lists commonly associated with Hispanic Americans
and that have been previously investigated in national survey studies (Experian, Innovation,
Nielsen, Dato-on). There is no previous study that has qualitatively obtained a list with
commonly used items and empirically tested hypothesized relationships in this shopping data
using a Market Basket methodology.
C. Findings
The Market Basket data was analyzed using RapidMiner, an environment for machine
learning, data mining, text mining, predictive analytics, and business analytics
(www.rapidminer.com.) For this relatively low sample (n = 15) a confidence level of 1 was set.
Information gain ratio was also 1. Data was formatted in Excel and exported into RapidMiner
using a RapidMiner template consisting of nine process connectors (see Process template in
Appendix). Multiple iterations were run using this data mining technique resulting in strong
correlational associations consistent with Market Basket analytics.
7
Hypothesis one was strongly supported. Those items interpreted to have a relatively high
level of cultural meaning were strongly associated with culturally desirable items and a
discernible pattern emerged in the data. Associations related to this hypothesis had a support
index of 46% (.469, p-2 .249, lift 2.143). This suggests that these pairs of items were frequently
shopped by shoppers and included in the same basket at a 46%. Items in this category include
maize, tequila, tortilla, taco, etc. Culturally, these items are very important for HispanicAmericans and consistent with the literature, they remind Hispanic-Americas of their culture
and values.
Hypothesis two was also moderately supported. Items with Spanish sounded words
clustered meaningfully and yielded a relatively high index of support or correlation, i.e., 40%
(.40, p-2, .213, lift 2.143). These items are usually labeled in Spanish: queso fresco, queso
blanco, etc.). It makes sense that Hispanic-Americans would demonstrate a preference for
familiarity and Spanish labels very likely elicited a shopping behavior in this particular sample.
Hypothesis 3 was moderately supported. An association emerged with those items that
are typically consumed in family contexts, for example, at a fiesta (party). These items include:
carne asada, corona, bottled tea, spirits wine). Though comparatively lower than the previous
association indexes, these items were purchased concurrently in the same basket at a rate of 6%
(.06, p-2, .036, lift 2.143).
Hypothesis 4 was not supported. A close analysis of items with a more Americanized
connotation, or that based on empirical studies are more commonly purchased by Americans
(e.g., carbonated drinks, shortening oil, mazzarrella versus queso fresco, tea) were not less
frequently purchased by Hispanic-Americans in comparison to other items in this basket. This
hypothesis warrants further research. It may be possible to do a similar study with two market
8
baskets ideally with three ethnic groups: self-identified U.S. Americans, highly acculturated
Hispanic Americans, and less acculturated Hispanic-Americans. A comparison with these three
groups would yield data that would allow for a more meaningful comparison on shopping
behaviors and their cross cultural differences.
Similarly, there was no association in items purchased that would support hypothesis 5.
There was no discernible association between and among items that would suggest a preference
for brands. It may well be that these items do not carry distinctive brands as the ones preferred
by Hispanic-Americans. Future analyses may examine market baskets that contain clearly
branded products and a comparison can be made between these and non-branded items. A larger
number of items for a more robust market basket analysis is also recommended (Huang &
Chang, 2010; Koslowsky, 2006).
d) Business Implications
These findings have potential implications for businesses, marketing, and retail shopping
in particular. Strategic marketing campaigns consistent with hypothesis one would benefit from
including in their inventories items and products that have a “cultural pull” for customers. More
specifically, businesses need to include products that are familiar to Hispanic-Americans, one of
the emergent markets in the U.S. At a more fundamental and practical level, a business may
increase inventory of items around culturally significant holidays when Hispanic-American
shoppers will intentionally seek out products that are culturally meaningful for them.
The literature suggests that Spanish is of paramount importance for many HispanicAmerican, particularly for less acculturated U.S. Hispanics (Ebenkamp & Nolan, 2001). It is
recommended that businesses increase the use of Spanish as a strategy to market and target this
strong U.S. market. This may include signage, labels, commercials, and other media to reach
9
this ethnic group. A bilingual strategy would be equally effective. Businesses may select
particular phrases in Spanish that have a high level of cultural content, for example with
references to families and collectivistic values. This will most likely have a strong appeal to the
sensibilities of this population. Similarly and consistent with hypothesis 3, businesses need to
focus on those items that have a strong family orientation. Strategically, they may place these
items near areas where children and the elderly can comfortably walk, sit, or “browse.”
Conclusion
Market Basket Analysis is a robust analytic methodology that can be widely used by
businesses as they seek out strategies to target specific markets. In the case of ethnic minorities
in the United States with strong emerging markets, businesses can collect data and conduct data
mining analyses that will yield interpretable patterns. This knowledge can then be strategically
utilized for marketing and most importantly to maximize the revenue and profits obtained from
these segmented markets.
The findings from this data analysis may generalize across other culturally, linguistically,
and racially diverse groups. The Asian American population is comparably a strong emergent
market in the United States. Future research can conduct extensive market basket analyses
among these U.S. Asian groups, and do comparative studies so that businesses and marketers
can tailor their products and businesses practices based on specific values. For example, most
U.S. Asian Americans endorse cultural orientations similar to U.S. Hispanics with an emphasis
on respect for the elderly, filial piety, collectivism, and other family values. Similar marketing
strategies can be used with African Americans, the second largest ethnic group in the United
States.
10
The most important finding from this Market Basket analysis is that retailers and
businesses can demonstrate culturally sensitive practices and use them effectively to attract
more shoppers and concurrently to provide an enjoyable experience for these diverse
individuals. Business intelligence is an effective tool that can be deployed intelligently by any
business, especially those in ethnically diverse regions of the country.
References
Athappillly, K., Razi, M. A., & Tarn, M. (2010). A multi-technique data mining approach to
exploring consumer behaviors. Human Systems Management, 29, 153-163.
Chain Store Age. March 2006, 82 (3), 26. Discount stores are muy bueno.
Chain Store Age. June 98, 74 (6), 64, 3. Data mining is more than beer and diapers.
Crispell, D. (1997). Hispanics at the mall. American Demographics, 19 (10), 35.
Dasari, S., & Kurkehar, A. (2011). Retail analytics using SAS: Experience of Fresh greens Pvt.
Ltd., Bangalore. Journal of Supply Chain Management, 8 (1), 7 – 22.
Dato-on, M. C., Burns, D. J., Manolis, C., & Hanvanich, S. (2006). Cultural assimilation and its
effects on consumption behaviors: An examination of shopping behaviors and preferences of
U.S. Hispanic immigrants. Journal of Shopping Center Research, 13 (2), 107-138.
Decker, R., & Monien, K. (2003). Market basket analysis with neural gas networks and selforganising maps. Journal of Targeting, Measurement, & Analysis for Marketing, 11 (4), 373.
Donthu, N. , & Cherian, J. (1994). Impact of strength of ethnic identification on Hispanic
shopping behavior. Journal of Retailing, 70 (4), 383-393.
Ebenkamp, B., & Nolan, A. A. (2001). Mexi-Melting Pot?. Brandweek, 42 (33).
Enis, M. (2009). Baking tradition. Supermarket News, 57 (22), 1-2.
Experian. The New American Consumer: Trends in Hispanic Consumer Demographics
http://www.experian.com/simmons-research/trends-in-hispanic-consumer-demographics.html
Accessed on December 10, 2012.
Fisher, C. (1993). Poll: Hispanics stick to brands. Advertising Age, 64 (7), 6.
Harikumar, L., & Nagadevara, V. (2012). Analytics: A competitive edge for a retail portal.
Journal of the Academy of Business & Economics, 12 (1), 43-48.
11
Huang, L-W., & Chang, Y-I (2010). A graph-based approach for mining closed large item sets.
International Journal of Organizational Innovation, 3 (2), 401-415.
Innovation. Center for U.S. Dairy.
http://www.dairyfoods.com/ext/resources/Digital_Brochures/DF-Hispanic-White-PaperFINAL.pdf Accessed on December 8, 2012.
Koslowsky, S. (2006). Market-Basket Mystery. Direct, 18, (12), 60-63.
Mellgren, J. (2010). The ethnic audience: Marketing to a brave new world. Gourmet Retailer, 31
(8), 15-17.
Mulhern, F. J., & Williams, J. D. (1994). A comparative analysis of shopping behavior in
Hispanic and non-Hispanic market areas. Journal of Retailing, 70 (3), 231-251.
Nielsen. State of Hispanic Consumer Report
http://es.nielsen.com/site/documents/State_of_Hispanic_Consumer_Report_4-16-FINAL.pdf
Accessed on December 10, 2012
Nicholls, J. A. F., Roslow, S., & Dublish, S. (1997). Time and companionship: Key factors in
Hispanic shopping behavior. Journal of Consumer Marketing, 14 (2), 194-205.
Rapid miner. http://rapid-i.com/content/view/181/190/ Accessed on December 6, 2012.
Rivkin, J. (2007). Noticias Nuevas. Private Label Buyer, 21 (10), 4.
Shim, S., & Eastlick, M. A. (1998). The hierarchical influence of personal values on mall
shopping attitude and behavior. Journal of Retailing, 74 (1), 139-160.
Silent Revolution. Business Latin America (2002), 37 (48), 7.
12
Appendix: RapidMiner Outputs
A. Process
B: Syntax
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.2.008">
<context>
<input/>
<output/>
<macros/>
</context>
13
<operator activated="true" class="process" compatibility="5.0.000" expanded="true"
name="Process">
<description>Reads a data set containing of three columns: customerId, itemId, and
itemCount. The item count is summed up per item and customer, pivoting is performed to have
one attribute per item, and finally, association rules are generated.</description>
<process expanded="true" height="578" width="840">
<operator activated="true" class="read_excel" compatibility="5.2.008" expanded="true"
height="60" name="Read Excel" width="90" x="45" y="30">
<parameter key="excel_file" value="C:\Users\ortiz2\Documents\Market Basket
Analysis\Data Ortiz.xls"/>
<parameter key="sheet_number" value="3"/>
<parameter key="imported_cell_range" value="A1:C91"/>
<parameter key="first_row_as_names" value="false"/>
<list key="annotations">
<parameter key="0" value="Name"/>
</list>
<list key="data_set_meta_data_information">
<parameter key="0" value="customerId.true.integer.attribute"/>
<parameter key="1" value="itemId.true.integer.attribute"/>
<parameter key="2" value="itemCount.true.integer.attribute"/>
</list>
</operator>
<operator activated="true" class="set_macro" compatibility="5.0.000" expanded="true"
height="76" name="Define Item Count" width="90" x="179" y="30">
<parameter key="macro" value="%{itemCountAttributeName}"/>
<parameter key="value" value="itemCount"/>
</operator>
14
<operator activated="true" class="set_macro" compatibility="5.0.000" expanded="true"
height="76" name="Define Customer" width="90" x="313" y="30">
<parameter key="macro" value="customerIdAttributeName"/>
<parameter key="value" value="customerId"/>
</operator>
<operator activated="true" class="set_macro" compatibility="5.0.000" expanded="true"
height="76" name="Define Item" width="90" x="447" y="30">
<parameter key="macro" value="itemIdAttributeName"/>
<parameter key="value" value="itemId"/>
</operator>
<operator activated="true" class="aggregate" compatibility="5.0.000" expanded="true"
height="76" name="Aggregate" width="90" x="45" y="210">
<list key="aggregation_attributes">
<parameter key="itemCount" value="sum"/>
</list>
<parameter key="group_by_attributes" value="customerId|itemId"/>
</operator>
<operator activated="true" class="pivot" compatibility="5.0.000" expanded="true"
height="76" name="Pivot" width="90" x="179" y="210">
<parameter key="group_attribute" value="customerId"/>
<parameter key="index_attribute" value="itemId"/>
</operator>
<operator activated="true" class="set_role" compatibility="5.0.000" expanded="true"
height="76" name="Set Role" width="90" x="313" y="210">
<parameter key="name" value="customerId"/>
<parameter key="target_role" value="id"/>
<list key="set_additional_roles"/>
15
</operator>
<operator activated="true" class="replace_missing_values" compatibility="5.2.008"
expanded="true" height="94" name="Replace Missing Values" width="90" x="447" y="210">
<parameter key="default" value="zero"/>
<list key="columns"/>
</operator>
<operator activated="true" class="numerical_to_binominal" compatibility="5.2.008"
expanded="true" height="76" name="Numerical to Binominal" width="90" x="581" y="210"/>
<operator activated="true" class="fp_growth" compatibility="5.2.008" expanded="true"
height="76" name="FP-Growth" width="90" x="581" y="75">
<parameter key="positive_value" value="true"/>
<parameter key="min_support" value="1.0E-4"/>
</operator>
<operator activated="true" class="create_association_rules" compatibility="5.2.008"
expanded="true" height="76" name="Create Association Rules" width="90" x="715" y="75">
<parameter key="min_confidence" value="0.01"/>
</operator>
<connect from_op="Read Excel" from_port="output" to_op="Define Item Count"
to_port="through 1"/>
<connect from_op="Define Item Count" from_port="through 1" to_op="Define Customer"
to_port="through 1"/>
<connect from_op="Define Customer" from_port="through 1" to_op="Define Item"
to_port="through 1"/>
<connect from_op="Define Item" from_port="through 1" to_op="Aggregate"
to_port="example set input"/>
<connect from_op="Aggregate" from_port="example set output" to_op="Pivot"
to_port="example set input"/>
<connect from_op="Pivot" from_port="example set output" to_op="Set Role"
to_port="example set input"/>
16
<connect from_op="Set Role" from_port="example set output" to_op="Replace Missing
Values" to_port="example set input"/>
<connect from_op="Replace Missing Values" from_port="example set output"
to_op="Numerical to Binominal" to_port="example set input"/>
<connect from_op="Numerical to Binominal" from_port="example set output" to_op="FPGrowth" to_port="example set"/>
<connect from_op="FP-Growth" from_port="frequent sets" to_op="Create Association
Rules" to_port="item sets"/>
<connect from_op="Create Association Rules" from_port="rules" to_port="result 1"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="180"/>
<portSpacing port="sink_result 2" spacing="0"/>
</process>
</operator>
</process>
17
C: Output
D: Association Rules
AssociationRules
Association Rules
[sum(itemCount)_9]
[sum(itemCount)_9]
[sum(itemCount)_9]
[sum(itemCount)_9]
[sum(itemCount)_9]
[sum(itemCount)_9]
[sum(itemCount)_9]
[sum(itemCount)_9]
[sum(itemCount)_9]
[sum(itemCount)_9]
[sum(itemCount)_9]
[sum(itemCount)_9]
-->
-->
-->
-->
-->
-->
-->
-->
-->
-->
-->
-->
[sum(itemCount)_12] (confidence: 0.143)
[sum(itemCount)_6] (confidence: 0.143)
[sum(itemCount)_19] (confidence: 0.143)
[sum(itemCount)_4] (confidence: 0.143)
[sum(itemCount)_23] (confidence: 0.143)
[sum(itemCount)_24] (confidence: 0.143)
[sum(itemCount)_21] (confidence: 0.143)
[sum(itemCount)_20] (confidence: 0.143)
[sum(itemCount)_22] (confidence: 0.143)
[sum(itemCount)_18] (confidence: 0.143)
[sum(itemCount)_17] (confidence: 0.143)
[sum(itemCount)_15] (confidence: 0.143)
18
[sum(itemCount)_9]
[sum(itemCount)_7]
[sum(itemCount)_7]
[sum(itemCount)_7]
[sum(itemCount)_7]
[sum(itemCount)_7]
[sum(itemCount)_7]
[sum(itemCount)_7]
[sum(itemCount)_7]
[sum(itemCount)_7]
[sum(itemCount)_7]
[sum(itemCount)_7]
[sum(itemCount)_7]
[sum(itemCount)_7]
[sum(itemCount)_9]
[sum(itemCount)_7]
[sum(itemCount)_9,
[sum(itemCount)_9]
[sum(itemCount)_7]
[sum(itemCount)_9,
[sum(itemCount)_9]
[sum(itemCount)_7]
[sum(itemCount)_9,
[sum(itemCount)_9]
[sum(itemCount)_7]
[sum(itemCount)_9,
[sum(itemCount)_9]
[sum(itemCount)_7]
[sum(itemCount)_9,
[sum(itemCount)_9]
[sum(itemCount)_7]
[sum(itemCount)_9,
[sum(itemCount)_9]
[sum(itemCount)_7]
[sum(itemCount)_9,
[sum(itemCount)_9]
[sum(itemCount)_7]
[sum(itemCount)_9,
[sum(itemCount)_9]
[sum(itemCount)_7]
[sum(itemCount)_9,
[sum(itemCount)_9]
[sum(itemCount)_7]
[sum(itemCount)_9,
[sum(itemCount)_9]
[sum(itemCount)_7]
[sum(itemCount)_9,
[sum(itemCount)_9]
[sum(itemCount)_7]
[sum(itemCount)_9,
[sum(itemCount)_9]
[sum(itemCount)_7]
[sum(itemCount)_9,
[sum(itemCount)_9]
[sum(itemCount)_9]
[sum(itemCount)_9]
[sum(itemCount)_9]
[sum(itemCount)_9]
[sum(itemCount)_9]
[sum(itemCount)_9]
[sum(itemCount)_9]
[sum(itemCount)_9]
[sum(itemCount)_9]
--> [sum(itemCount)_13] (confidence: 0.143)
--> [sum(itemCount)_12] (confidence: 0.143)
--> [sum(itemCount)_6] (confidence: 0.143)
--> [sum(itemCount)_19] (confidence: 0.143)
--> [sum(itemCount)_4] (confidence: 0.143)
--> [sum(itemCount)_23] (confidence: 0.143)
--> [sum(itemCount)_24] (confidence: 0.143)
--> [sum(itemCount)_21] (confidence: 0.143)
--> [sum(itemCount)_20] (confidence: 0.143)
--> [sum(itemCount)_22] (confidence: 0.143)
--> [sum(itemCount)_18] (confidence: 0.143)
--> [sum(itemCount)_17] (confidence: 0.143)
--> [sum(itemCount)_15] (confidence: 0.143)
--> [sum(itemCount)_13] (confidence: 0.143)
--> [sum(itemCount)_7, sum(itemCount)_12] (confidence: 0.143)
--> [sum(itemCount)_9, sum(itemCount)_12] (confidence: 0.143)
sum(itemCount)_7] --> [sum(itemCount)_12] (confidence: 0.143)
--> [sum(itemCount)_7, sum(itemCount)_6] (confidence: 0.143)
--> [sum(itemCount)_9, sum(itemCount)_6] (confidence: 0.143)
sum(itemCount)_7] --> [sum(itemCount)_6] (confidence: 0.143)
--> [sum(itemCount)_7, sum(itemCount)_19] (confidence: 0.143)
--> [sum(itemCount)_9, sum(itemCount)_19] (confidence: 0.143)
sum(itemCount)_7] --> [sum(itemCount)_19] (confidence: 0.143)
--> [sum(itemCount)_7, sum(itemCount)_4] (confidence: 0.143)
--> [sum(itemCount)_9, sum(itemCount)_4] (confidence: 0.143)
sum(itemCount)_7] --> [sum(itemCount)_4] (confidence: 0.143)
--> [sum(itemCount)_7, sum(itemCount)_23] (confidence: 0.143)
--> [sum(itemCount)_9, sum(itemCount)_23] (confidence: 0.143)
sum(itemCount)_7] --> [sum(itemCount)_23] (confidence: 0.143)
--> [sum(itemCount)_7, sum(itemCount)_24] (confidence: 0.143)
--> [sum(itemCount)_9, sum(itemCount)_24] (confidence: 0.143)
sum(itemCount)_7] --> [sum(itemCount)_24] (confidence: 0.143)
--> [sum(itemCount)_7, sum(itemCount)_21] (confidence: 0.143)
--> [sum(itemCount)_9, sum(itemCount)_21] (confidence: 0.143)
sum(itemCount)_7] --> [sum(itemCount)_21] (confidence: 0.143)
--> [sum(itemCount)_7, sum(itemCount)_20] (confidence: 0.143)
--> [sum(itemCount)_9, sum(itemCount)_20] (confidence: 0.143)
sum(itemCount)_7] --> [sum(itemCount)_20] (confidence: 0.143)
--> [sum(itemCount)_7, sum(itemCount)_22] (confidence: 0.143)
--> [sum(itemCount)_9, sum(itemCount)_22] (confidence: 0.143)
sum(itemCount)_7] --> [sum(itemCount)_22] (confidence: 0.143)
--> [sum(itemCount)_7, sum(itemCount)_18] (confidence: 0.143)
--> [sum(itemCount)_9, sum(itemCount)_18] (confidence: 0.143)
sum(itemCount)_7] --> [sum(itemCount)_18] (confidence: 0.143)
--> [sum(itemCount)_7, sum(itemCount)_17] (confidence: 0.143)
--> [sum(itemCount)_9, sum(itemCount)_17] (confidence: 0.143)
sum(itemCount)_7] --> [sum(itemCount)_17] (confidence: 0.143)
--> [sum(itemCount)_7, sum(itemCount)_15] (confidence: 0.143)
--> [sum(itemCount)_9, sum(itemCount)_15] (confidence: 0.143)
sum(itemCount)_7] --> [sum(itemCount)_15] (confidence: 0.143)
--> [sum(itemCount)_7, sum(itemCount)_13] (confidence: 0.143)
--> [sum(itemCount)_9, sum(itemCount)_13] (confidence: 0.143)
sum(itemCount)_7] --> [sum(itemCount)_13] (confidence: 0.143)
--> [sum(itemCount)_8, sum(itemCount)_4] (confidence: 0.143)
--> [sum(itemCount)_12, sum(itemCount)_6] (confidence: 0.143)
--> [sum(itemCount)_12, sum(itemCount)_19] (confidence: 0.143)
--> [sum(itemCount)_12, sum(itemCount)_23] (confidence: 0.143)
--> [sum(itemCount)_12, sum(itemCount)_24] (confidence: 0.143)
--> [sum(itemCount)_12, sum(itemCount)_21] (confidence: 0.143)
--> [sum(itemCount)_12, sum(itemCount)_20] (confidence: 0.143)
--> [sum(itemCount)_12, sum(itemCount)_22] (confidence: 0.143)
--> [sum(itemCount)_12, sum(itemCount)_18] (confidence: 0.143)
--> [sum(itemCount)_12, sum(itemCount)_17] (confidence: 0.143)
19
[sum(itemCount)_9] --> [sum(itemCount)_12, sum(itemCount)_15] (confidence: 0.143)
[sum(itemCount)_9] --> [sum(itemCount)_12, sum(itemCount)_13] (confidence: 0.143)
[sum(itemCount)_9] --> [sum(itemCount)_6, sum(itemCount)_19] (confidence: 0.143)
[sum(itemCount)_9] --> [sum(itemCount)_6, sum(itemCount)_23] (confidence: 0.143)
[sum(itemCount)_9] --> [sum(itemCount)_6, sum(itemCount)_24] (confidence: 0.143)
[sum(itemCount)_9] --> [sum(itemCount)_6, sum(itemCount)_21] (confidence: 0.143)
[sum(itemCount)_9] --> [sum(itemCount)_6, sum(itemCount)_20] (confidence: 0.143)
[sum(itemCount)_9] --> [sum(itemCount)_6, sum(itemCount)_22] (confidence: 0.143)
[sum(itemCount)_9] --> [sum(itemCount)_6, sum(itemCount)_18] (confidence: 0.143)
[sum(itemCount)_9] --> [sum(itemCount)_6, sum(itemCount)_17] (confidence: 0.143)
[sum(itemCount)_9] --> [sum(itemCount)_6, sum(itemCount)_15] (confidence: 0.143)
[sum(itemCount)_9] --> [sum(itemCount)_6, sum(itemCount)_13] (confidence: 0.143)
[sum(itemCount)_9] --> [sum(itemCount)_19, sum(itemCount)_23] (confidence: 0.143)
[sum(itemCount)_9] --> [sum(itemCount)_19, sum(itemCount)_24] (confidence: 0.143)
[sum(itemCount)_9] --> [sum(itemCount)_19, sum(itemCount)_21] (confidence: 0.143)
[sum(itemCount)_9] --> [sum(itemCount)_19, sum(itemCount)_20] (confidence: 0.143)
[sum(itemCount)_9] --> [sum(itemCount)_19, sum(itemCount)_22] (confidence: 0.143)
[sum(itemCount)_9] --> [sum(itemCount)_19, sum(itemCount)_18] (confidence: 0.143)
[sum(itemCount)_9] --> [sum(itemCount)_19, sum(itemCount)_17] (confidence: 0.143)
[sum(itemCount)_9] --> [sum(itemCount)_19, sum(itemCount)_15] (confidence: 0.143)
[sum(itemCount)_9] --> [sum(itemCount)_19, sum(itemCount)_13] (confidence: 0.143)
[sum(itemCount)_9] --> [sum(itemCount)_23, sum(itemCount)_24] (confidence: 0.143)
[sum(itemCount)_9] --> [sum(itemCount)_23, sum(itemCount)_21] (confidence: 0.143)
[sum(itemCount)_9] --> [sum(itemCount)_23, sum(itemCount)_20] (confidence: 0.143)
[sum(itemCount)_9] --> [sum(itemCount)_23, sum(itemCount)_22] (confidence: 0.143)
[sum(itemCount)_9] --> [sum(itemCount)_23, sum(itemCount)_18] (confidence: 0.143)
... 5107856 other rules ...
20