我想在 RapidMiner 中处理这样的数据集：
order_id | 项目1 | 项目2 | 项目3
1 | 书 | 书 | 铅笔
2 | 铅笔| 高分辨率照片| CLIPARTO 书 | 橡皮
我想使用 fp-growth 和关联规则处理这些数据。什么是适合 RapidMiner 规则的合适数据集？
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1 回答
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您看过 RapidMiner 中 FP-Growth 算子的教程流程吗（点击帮助文本中的链接），您可以找到详细的示例流程。数据已经与您的示例非常相似 [1]
将此类结构化数据导入 RapidMiner 很容易。使用“导入数据”按钮，或使用“Turbo Prep”辅助工具加载和准备数据。
[1] 只需将 xml 复制到您的流程设计窗口中：
<?xml version="1.0" encoding="UTF-8"?><process version="9.1.000">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="9.1.000" expanded="true" name="Process" origin="GENERATED_TUTORIAL">
<parameter key="logverbosity" value="init"/>
<parameter key="random_seed" value="2001"/>
<parameter key="send_mail" value="never"/>
<parameter key="notification_email" value=""/>
<parameter key="process_duration_for_mail" value="30"/>
<parameter key="encoding" value="SYSTEM"/>
<process expanded="true">
<operator activated="true" breakpoints="after" class="retrieve" compatibility="9.1.000" expanded="true" height="68" name="Load Transactions" origin="GENERATED_TUTORIAL" width="90" x="112" y="187">
<parameter key="repository_entry" value="//Samples/Templates/Market Basket Analysis/Transactions"/>
</operator>
<operator activated="true" class="aggregate" compatibility="6.0.006" expanded="true" height="82" name="Aggregate" origin="GENERATED_TUTORIAL" width="90" x="112" y="336">
<parameter key="use_default_aggregation" value="false"/>
<parameter key="attribute_filter_type" value="all"/>
<parameter key="attribute" value=""/>
<parameter key="attributes" value=""/>
<parameter key="use_except_expression" value="false"/>
<parameter key="value_type" value="attribute_value"/>
<parameter key="use_value_type_exception" value="false"/>
<parameter key="except_value_type" value="time"/>
<parameter key="block_type" value="attribute_block"/>
<parameter key="use_block_type_exception" value="false"/>
<parameter key="except_block_type" value="value_matrix_row_start"/>
<parameter key="invert_selection" value="false"/>
<parameter key="include_special_attributes" value="false"/>
<parameter key="default_aggregation_function" value="average"/>
<list key="aggregation_attributes">
<parameter key="product 1" value="concatenation"/>
</list>
<parameter key="group_by_attributes" value="Invoice"/>
<parameter key="count_all_combinations" value="false"/>
<parameter key="only_distinct" value="false"/>
<parameter key="ignore_missings" value="true"/>
</operator>
<operator activated="true" class="rename" compatibility="9.1.000" expanded="true" height="82" name="Rename" origin="GENERATED_TUTORIAL" width="90" x="246" y="340">
<parameter key="old_name" value="concat(product 1)"/>
<parameter key="new_name" value="Products"/>
<list key="rename_additional_attributes"/>
</operator>
<operator activated="true" class="set_role" compatibility="9.1.000" expanded="true" height="82" name="Set Role" origin="GENERATED_TUTORIAL" width="90" x="380" y="340">
<parameter key="attribute_name" value="Invoice"/>
<parameter key="target_role" value="id"/>
<list key="set_additional_roles"/>
</operator>
<operator activated="true" breakpoints="before" class="concurrency:fp_growth" compatibility="9.1.000" expanded="true" height="82" name="FP-Growth" origin="GENERATED_TUTORIAL" width="90" x="648" y="289">
<parameter key="input_format" value="item list in a column"/>
<parameter key="item_separators" value="|"/>
<parameter key="use_quotes" value="false"/>
<parameter key="quotes_character" value="""/>
<parameter key="escape_character" value="\"/>
<parameter key="trim_item_names" value="true"/>
<parameter key="positive_value" value="true"/>
<parameter key="min_requirement" value="support"/>
<parameter key="min_support" value="0.005"/>
<parameter key="min_frequency" value="100"/>
<parameter key="min_items_per_itemset" value="1"/>
<parameter key="max_items_per_itemset" value="0"/>
<parameter key="max_number_of_itemsets" value="1000000"/>
<parameter key="find_min_number_of_itemsets" value="false"/>
<parameter key="min_number_of_itemsets" value="100"/>
<parameter key="max_number_of_retries" value="15"/>
<parameter key="requirement_decrease_factor" value="0.9"/>
<enumeration key="must_contain_list"/>
</operator>
<operator activated="true" class="create_association_rules" compatibility="9.1.000" expanded="true" height="82" name="Create Association Rules" origin="GENERATED_TUTORIAL" width="90" x="648" y="442">
<parameter key="criterion" value="confidence"/>
<parameter key="min_confidence" value="0.1"/>
<parameter key="min_criterion_value" value="0.8"/>
<parameter key="gain_theta" value="2.0"/>
<parameter key="laplace_k" value="1.0"/>
</operator>
<connect from_op="Load Transactions" from_port="output" to_op="Aggregate" to_port="example set input"/>
<connect from_op="Aggregate" from_port="example set output" to_op="Rename" to_port="example set input"/>
<connect from_op="Rename" from_port="example set output" to_op="Set Role" to_port="example set input"/>
<connect from_op="Set Role" from_port="example set output" to_op="FP-Growth" 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"/>
<connect from_op="Create Association Rules" from_port="item sets" to_port="result 2"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="147"/>
<portSpacing port="sink_result 2" spacing="0"/>
<portSpacing port="sink_result 3" spacing="42"/>
<description align="left" color="yellow" colored="false" height="70" resized="false" width="850" x="20" y="25">MARKET BASKET ANALYSIS<br>Model associations
between products by determining sets of items frequently purchased together and building
association rules to derive recommendations.
</description>
<description align="left" color="blue" colored="true" height="185" resized="true" width="550" x="20" y="105">Step 1:<br/>Load transaction data containing a
transaction id, a product id and a quantifier. The data denotes how many times a certain
product has been purchased as part of a transactions.
</description>
<description align="left" color="purple" colored="true" height="341" resized="true" width="549" x="20" y="300"><br> <br> <br> <br> <br>
<br> <br> <br> Step 2:<br>Edit, transform &amp; load (ETL) -
Aggregate transaction data via concatenation so that the products in a transaction are
in one entry, separated by the pipe symbol.<br>
</description>
<description align="left" color="green" colored="true" height="310" resized="true" width="290" x="580" y="105">Step 3:<br/>Using FP-Growth, determine
frequent item sets. A frequent item sets denotes that the items (products) in the set
have been purchased together frequently, i.e. in a certain ratio of transactions. This
ratio is given by the support of the item set.
</description>
<description align="left" color="green" colored="true" height="215" resized="true" width="286" x="579" y="425"><br> <br> <br> <br> <br>
<br> Step 4:<br/>Create association rules which can be used for product
recommendations depending on the confidences of the rules.<br>
</description>
<description align="left" color="yellow" colored="false" height="35" resized="true" width="849" x="20" y="655">Outputs: association rules, frequent item set<br>
</description>
</process>
</operator>
</process>
于 2019-01-03T16:32:34.940 回答