(12) United States Patent
`Schwenkreis
`
`(10) Patent N0.:
`(45) Date of Patent:
`
`US 6,553,359 B1
`Apr. 22, 2003
`
`US006553359B1
`
`(54) DATA MINING FOR ASSOCIATION RULES
`AND SEQUENTIAL PATTERNS WITHIN
`DATA OF INHOMOGENEOUS TYPE
`
`5,933,818 A *
`
`8/1999 Kasraviet 61.
`
`............. .. 706/12
`
`* cited by examiner
`
`(75)
`
`Inventor:
`
`Friedemann Schwenkreis;
`Leinfelden-Echterdingen (DE)
`
`(73) Assignee:
`
`International Business Machines
`Corporation, Armonk; NY (US)
`
`( * ) Notice:
`
`Subject to any disclaimer, the term of this
`patent is extended or adjusted under 35
`U.S.C. 154(b) by 0 days.
`
`(21) Appl. No.: 09/559,617
`
`(22)
`
`Filed:
`
`Apr. 27, 2000
`
`(30)
`
`Foreign Application Priority Data
`
`Apr. 27, 1999
`
`(EP) .......................................... .. 99108219
`
`Int. Cl.7 ................................................ .. G06N 5/02
`(51)
`(52) U.s. Cl.
`............................... .. 706/46; 707/1; 707/6;
`707/7
`
`(58) Field of Search ............................ .. 706/45; 46; 47;
`700/49; 707/1, 6,7
`
`(56)
`
`References Cited
`U.S. PATENT DOCUMENTS
`
`Primary Examiner—Thomas Black
`Assistant Examiner—Joseph P. Hirl
`(74) Attorney, Agent, or Firm—Ronald L. Drumheller;
`Khanh Q. Tran
`
`(57)
`
`ABSTRACT
`
`The invention relates to a computerized method for data
`mining for association rules and/or sequential patterns in a
`multitude of records. The invention is applicable to records
`that
`include a transaction-identification and at
`least one
`
`transaction-item with a corresponding item-type, wherein
`the multitude of records include transaction-items to be
`
`item-types. The method further
`mined having different
`includes a preprocessing step for transforming each record
`into one or more transaction-records in transaction-format.
`For each transaction-item to be data mined in a record, a
`transaction-record is generated and the transaction-record
`includes at least the transaction-identification of the record
`and an encoded transaction-item. The encoded transaction-
`
`item encodes the transaction-item and its corresponding
`item-type into one value. Finally the method includes a
`mining step wherein a state of the art data-mining technique
`is applied to the transaction-records for data mining for
`association rules and/or sequential patterns.
`
`5,832,482 A * 11/1998 Yu et al. ...................... .. 707/1
`
`7 Claims, 3 Drawing Sheets
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`U.S. Patent
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`Apr. 22, 2003
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`Sheet 2 of3
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`US 6,553,359 B1
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`31 0
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`TA
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`Item
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`U.S. Patent
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`Apr. 22, 2003
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`Sheet 3 of3
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`US 6,553,359 B1
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`US 6,553,359 B1
`
`1
`DATA MINING FOR ASSOCIATION RULES
`AND SEQUENTIAL PATTERNS WITHIN
`DATA OF INHOMOGENEOUS TYPE
`
`BACKGROUND OF THE INVENTION
`
`1. Field of the Invention
`
`The present invention relates to data mining technology.
`More particularly,
`it relates to the area of mining for
`association rules and/or sequential patterns within data
`assets.
`
`2. Description and Disadvantages of Prior Art
`Over the past two decades there has been a huge increase
`in the amount of data being stored in databases as well as the
`number of database applications in business and the scien-
`tific domain. This explosion in the amount of electronically
`stored data was accelerated by the success of the relational
`model for storing data and the development and maturing of
`data retrieval and manipulation technologies. While tech-
`nology for storing the data developed fast to keep up with
`the demand, little stress was paid to developing software for
`analyzing the data until recently when companies realized
`that hidden within these masses of data was a resource that
`
`was being ignored. The huge amounts of stored data con-
`tains knowledge about a number of aspects of their business
`waiting to be harnessed and used for more effective business
`decision support. Database Management Systems used to
`manage these data sets at present only allow the user to
`access information explicitly present in the databases i.e. the
`data. The data stored in the database is only a small part of
`the ‘iceberg of information’ available from it. Contained
`implicitly within this data is knowledge about a number of
`aspects of their business waiting to be harnessed and used
`for more effective business decision support. This extraction
`of knowledge from large data sets is called Data Mining or
`Knowledge Discovery in databases and is defined as the
`non-trivial extraction of implicit, previously unknown and
`potentially useful information from data. The obvious ben-
`efits of Data Mining has resulted in a lot of resources being
`directed towards its development.
`Data mining involves the development of tools that ana-
`lyze large databases to extract useful information from them.
`As an application of data mining, customer purchasing
`patterns may be derived from a large customer transaction
`database by analyzing its transaction records. Such purchas-
`ing habits can provide invaluable marketing information.
`For example, retailers can create more effective store dis-
`plays and more effective control inventory than otherwise
`would be possible if they know consumer purchase patterns.
`As a further example, catalog companies can conduct more
`effective mass mailings if they know that, given that a
`consumer has purchased a first item, the same consumer can
`be expected, with some degree of probability, to purchase a
`particular second item within a particular time period after
`the first purchase.
`Data mining uses several techniques to find pieces of
`knowledge in large amounts of data. Two of these techniques
`are the so-called mining for association rules and the mining
`for sequential patterns.
`Identifying association rules from a large database of
`transactions is an essential part of data mining. An associa-
`tion rule is an expression of the form X—>Y, where X and Y
`are sets of items. In the retail domain, the data to be mined
`typically consist of transactions, where each transaction is
`characterized by a set of items. For example, the database
`may contain customers’ sale transactions on shoes and
`
`2
`jackets. Apossible association rule may be of the form “30
`percent of transactions that contain jackets also contain
`shoes; 10 percent of all transactions contain both shoes and
`jackets”. The 30 percent value is referred to as the confi-
`dence of the rule, while the 10 percent value is the support
`of the rule. The task of mining association rules involves
`finding all the association rules from the transactions that
`satisfy certain user-specified minimum support and confi-
`dence constraints.
`
`Conceptually, the problem may be viewed as finding the
`association rules from a relational table of records. Each
`
`record may represent a transaction, as in the case of a retail
`transaction database, or other data items in the database.
`Each record has one or more attributes where each attribute
`
`corresponds to an item of the transaction.
`Another essential part of data mining relates to identifi-
`cation of sequential pattern. This involves rules that are
`based on temporal data. Suppose we have a database of
`natural disasters. From such a database if we conclude that
`
`whenever there was an earthquake in Los Angeles, the next
`day Mt. Kilimanjaro erupted, such a rule would be a
`sequence rule. Such rules are useful for making predictions
`which could be useful in making market gains or for taking
`preventive action against natural disasters. The factor that
`differentiates sequence rules from other rules is the temporal
`factor.
`
`Other applications of data mining include catalog design,
`add-on sales, store layout, and customer segmentation based
`on buying patterns and many more. Typically the databases
`involved in these applications are very large. It is imperative,
`therefore, to have fast algorithms for this task.
`Although several methods of mining for association rules
`and mining for sequential patterns have been proposed, only
`methods derived from the so-called APRIORI approach (see
`R. Agrawal, S. Rikant, Fast Algorithms for Mining Asso-
`ciation Rules, in Proceedings of the 20th VLDB Conference,
`1994) have been proven to be efficient enough to process
`large data volumes.
`The APRIORI approach depends on a special format of
`the data called transaction format. In case of associations the
`
`transaction format conceptually consists of only two
`columns, namely a “transaction identifier” and an “item
`identifier”. In case of sequential patterns conceptually it
`consists of three columns, namely a “transaction group
`identifier”, a “transaction identifie”, and an “item identifier”.
`A much more serious drawback of the APRIORI approach
`according the current state of the art is that it requires that
`all of the “item identifiers” relate to the same item type. As
`a result the APRIORI approach is only capable of deriving
`association rules or sequences between items of the same
`type. If for instance the item identifier relates to a certain
`product bought by a certain customer the APRIORI tech-
`nique would be capable of deriving only rules of the form:
`if a customer buys PRODUCT1 then he also will buy
`PRODUCT2 with the probability of X%. The APRIORI
`approach would not be able include in its generated rules
`items of other types, like for instance the gender, the age, the
`profession, the place of residence or other aspects of the
`customers.
`It can be expected that once a multitude of
`different
`item types can be included in the process of
`derivation of rules the importance of the derived rules can be
`significantly increased as they would be much more selec-
`tive in nature.
`
`OBJECTIVE OF THE INVENTION
`
`The invention is based on the objective to provide a
`computerized method for data mining for association rules
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`US 6,553,359 B1
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`3
`and or sequential patterns of a multitude of records, wherein
`the multitude of records comprise transaction-items of dif-
`ferent item-types.
`SUMMARY AND ADVANTAGES OF THE
`INVENTION
`
`The objectives of the invention are solved by the inde-
`pendent claims. Further advantageous arrangements and
`embodiments of the invention are set forth in the respective
`subclaims.
`
`The invention relates to a computerized method for data
`mining for association rules and/or sequential patterns of a
`multitude of records. The invention is applicable to records
`comprising a transaction-identification and at
`least one
`transaction-item with a corresponding item-type wherein
`said multitude of records comprise transaction-items of
`different
`item-types. The proposed method further com-
`prises a preprocessing-step for transforming each record into
`one or more transaction-records of transaction-format.
`
`According to said transaction format for each transaction-
`item in said record a transaction-record is generated and said
`transaction-record comprises at
`least
`the transaction-
`identification of said record and an encoded transaction-item
`
`encoding said transaction-item and its corresponding item-
`type into one value. Finally said method comprises a
`mining-step wherein a state of the art data-mining tech-
`niques is applied to said transaction-records for data mining
`for association rules and/or sequential patterns.
`The current invention extends data mining technology
`according to the current state of the art and is now also
`supporting the mining for association rules and/or sequential
`patterns based on data assets comprising items of a multi-
`tude of item types. While current activities in this area of
`technology are concentrating on the search for new and
`advanced mining algorithms the current invention is able to
`achieve this goal by features pointing in a completely
`different and surprising direction. Instead of proposing a
`new mining algorithm the current invention suggests a new
`pre-processing step which transforms the data to be mined
`into a new encoding scheme. The usage of multiple fields to
`be defined as item fields for efficient mining for association/
`sequential patterns is supported without a need to introduce
`a new algorithm because data is not in transaction format.
`Thus mining algorithms proved to be very efficient and
`optimized during the last years are still applicable.
`BRIEF DESCRIPTION OF THE DRAWINGS
`
`FIG. 1 depicts an example of a typical database table data
`mining technology is applied to. The table is made up of a
`multitude of records which in turn are made up of a
`multitude of fields (representing the table columns).
`FIG. 2 reflects the transformation result of the table of
`FIG. 1 into transaction format. FIG. 2a visualizes the
`
`transaction format adapted for mining of association rules
`while FIG. 2b portrays the “transaction format” adapted for
`mining of sequential pattern.
`FIG. 3 visualizes the only solution known in the state of
`the art which is capable of treating database records with
`more than one item. This solution is limited to the case
`
`where all items relate to the same item type.
`FIG. 4 visualizes how the preprocessing step for trans-
`forming each database record into one or more transaction
`records of transaction format according the current teaching
`of encoded transaction items works.
`
`FIG. 5 depicts the complete preprocessing result of the
`introduced teaching with the encoded transaction items
`
`4
`based on the example of FIG. 1. In this case the item types
`(i.e.
`the columns of the table) Age, State and Item were
`selected as item columns and the TA column was selected as
`the transaction id column.
`
`DESCRIPTION OF THE PREFERRED
`EMBODIMENT
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`If the current invention is referring to a database (for
`instance a relational database), a table or a record these
`terms have to be understood from a conceptually point of
`view only. The term database has to be understood in its
`most general meaning referring to any amount of data. The
`data may be stored in a flat file or in an actual database.
`Moreover the current teaching does not require that the data
`is stored persistently. The current teaching may also be
`applied to volatile data stored somewhere in in-core
`memory. Also it is not necessary that the data actually is
`physically organized as a table structure made out of
`records. For the current invention it is insufficient that the
`
`data can being viewed from a logical point of view orga-
`nized in tables made out of records with a multitude of
`individual record fields.
`Introduction
`
`Typically the source data to which data mining technol-
`ogy is applied to is made available as tables made up of a
`multitude of records which in turn are made up of a
`multitude of fields (representing the table columns). FIG. 1
`depicts an example of such a table. The individual records of
`the table comprise the following individual fields: Customer,
`TA, Age, Gender, State, Item. The significance of most fields
`follows directly from the naming; the field “TA” contains the
`unique transaction identification through which a particular
`customer ordered a certain product identified by the “Item”
`field.
`
`Data mining technology like the APRIORI methodology
`for mining for association rules or for mining for sequential
`patterns cannot be applied to the original source format of
`the data like the table structure of FIG. 1. The APRIORI
`
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`approach for instance depends on a special format of the data
`called transaction format which differs from usual multi-
`column format of FIG. 1. In case of associations the trans-
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`action format consists of only two columns, namely a
`“transaction identifier” and an “item identifier”. In case of
`
`sequential patterns it consists of three columns, namely a
`“transaction group identifier”, a “transaction identifier”, and
`an “item identifier”. FIG. 2 reflects the transformation result
`of the table of FIG. 1 into transaction format. FIG. 2a
`
`visualizes the transaction format adapted for mining of
`association rules while FIG. 2b portrays the transaction
`format adapted for mining of sequential pattern. It is impor-
`tant to realize that according the current state of the art all
`item values in the transaction format representation relate to
`a single item type only; in the current example the item type
`is the product ordered by the customer.
`In both cases the item identifier must be treated as a
`
`so-called categorical variable. This means that the values of
`the item identifier are treated as strings. Given this kind of
`input data, the APRIORI based algorithms for association
`rules will find rules like:
`
`If a transaction contains itemo and itemp and itemq
`and .
`.
`.
`,
`.
`.
`then it will also contain itemm, item”, .
`Similarly, the APRIORI based algorithms for sequential
`patterns will find patterns like:
`.
`.
`Transactions containing itemo and itemp and .
`are followed by transactions containing itemp and .
`are followed by transactions containing .
`.
`.
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`US 6,553,359 B1
`
`5
`With the current state-of-the-art approaches it is impos-
`sible to use the efficient APRIORI-based algorithms or other
`similar approaches for mining association rules or sequential
`patterns with more than one item field. Thus according the
`current state of the art,
`like the APRIORI based
`methodologies, are unable to derive rules which in applica-
`tion to the current example would be able to associate the
`ordering of a certain PRODUCT1 (Item) and/or the age
`(Age) and/or the gender (Gender) and/or the state of resi-
`dence (State) of the customer with the probability of order-
`ing another PRODUCT2 (Item).
`The only solution known in the state which is capable of
`treating database records with more than one item is the case
`where all items relate to the same item type. An example of
`such a case is visualized in FIG. 3. According to this
`solutions the database table 301 can be transformed into
`transaction format even there is more than one item field 302
`to 305 because the type and the semantics of the item fields
`are all the same. In this special case a method called “pivot”
`can be used to transform the multi-field input
`into the
`classical transaction format reflected in FIG. 3 as 310. As
`
`can be seen from FIG. 3 every record of the multi-column
`data base is transformed in a multitude of transaction records
`
`having transaction format. For every item in a certain record
`of the multi-column table an individual transaction record is
`
`generated where all transaction records resulting from the
`same record of the multi-column database table refiect the
`same transaction identification.
`
`The Fundamental Encoding Scheme for Handling a Multi-
`tude of Item Types
`Given the input data of the table in FIG. 1, it is desirable
`that the well-known mining techniques for association rules
`and sequential patterns could be used to find rules like “if
`State is MA,
`then Item is 20” and respectively to find
`sequential patterns like “State is MA and Item is 20 is
`followed by State is CA and Item is 30”. These kind of
`results are different from the current results in the sense that
`
`not only attribute values appear in rules or patterns (MA,
`CA, 20, 30). With multi-column data and items of different
`types it is important to have also the column names (for
`example: State and Item) as part of the result.
`,
`.
`.
`.
`A multi-column table is a set of n columns {C1, C2,
`cn} (n>2). Each column ck has an identifier ik and a type tk.
`Efficient algorithms for association rules can only process
`two-column tables {C1, C2} and use one column as a
`so-called transaction identifier (or transaction identification)
`and the other column as the item identifier (or transaction
`item). Likewise, sequential patterns algorithms use three-
`column tables {C1, C2, C3} and use one column as a so-called
`transaction group identifier while and the semantic of the
`remaining two columns correspond to that of the association
`rule algorithm (comprising a transaction identifier and an
`item identifier).
`Since the main objective of this invention is to allow the
`usage of multiple columns as items and thus extend the
`known mining methodologies with the capability to derive
`rules between items of different types, we need to define also
`an abstract notion for item values, i.e. the values of the item
`identifiers. Given a column ck, and a record number m we
`denote the value of the column ck in that specific record with
`val(ck,
`For instance, the value of the column “State” in
`the first record of the table in FIG. 1 is CA: val(State,
`1)=CA. It is not necessary, that the val function returns the
`original values in the data. It might also be the case, that a
`value mapping is used to map continuos values to interval
`(discretization).
`The basic idea of the current invention is to teach the
`
`concept of a new transaction item, called encoded transac-
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`tion item. The encoded transaction item represents a single
`value which simultaneously encodes not only the transaction
`item but also the item type. Using such an approach it is
`guaranteed that transaction items of different item types
`always are represented by different values of the encoded
`transaction item. Operating on records set up according to
`this new transaction format state of the art mining method-
`ologies are transparently enabled to handle items of different
`item types as the encoding scheme will map different item
`types onto different encoded transaction items independent
`of the value of the transaction item. As will be shown below
`
`different mappings are available to encode the item type and
`the value of the transaction item into a single encoded
`transaction item.
`
`In more formal terms the presented mechanism introduces
`a mapping function map(i, val(i,
`which encodes the
`column identification and the value of an item column of
`
`record m in a single value. Two additional functions are
`introduced to define the behaviour of the mapping function:
`1. Column(encodedValue) will return the column of an
`encoded value generated with the map function.
`2. Value(encodedValue) will return the value of the item
`which was encoded by the map function.
`The mapping function must be designed such that for all
`pairs of i and m the following two conditions hold:
`1. Column(map(i, val(i, m)))=i
`2. Value(map(i, val(i, m )))=val(i,m)
`or in other words, the encoded transaction item is uniquely
`decode-able into the corresponding value of the transaction
`item and the corresponding item type.
`Using the New Encoding Approach Within Data Mining
`As outlined above the idea of the invention is to transform
`multi-column data into the data format which can be handled
`
`by the existing algorithms without loosing any information
`contained in the original table.
`FIG. 4 visualizes how the preprocessing step for trans-
`forming each database record into one or more transaction
`records of transaction format according the current teaching
`of encoded transaction items works. Described in general
`terms and given a multi-column table with m columns out of
`which n columns (denoted C1, C2, .
`.
`. , cm) have been selected
`as input for item values wherein the items may represent any
`mixture of same or different item type, the method works as
`follows (refer also to FIG. 4):
`For each record in the input data containing columns C1,
`C2, .
`.
`.
`, C” the corresponding column identifications and
`values are extracted (401, 402). Then the encoding
`map(ik, val(ik,p)) is calculated (403). As output (404)
`the mechanism generates a two-column table for asso-
`ciations and a three-column table for sequential pat-
`terns containing (transaction identification, encoded
`transaction item) pairs and (transaction group, transac-
`tion identification, encoded transaction item) tuples
`respectively.
`Of course for the application of the current it doesn’t
`matter whether the output of the mapping mechanism is
`directly stored as a physical table of some sort database or
`not;
`i.e.
`the output finally processed by the data mining
`methodology may also be provided in the volatile computer
`memory only. It can also be implemented as a special cursor
`for the input data which does the transformation without any
`physical storage.
`A Potential Mapping Function
`Of course various mapping function are possible which
`satisfy above mentioned conditions.
`One example for the mapping function a function can be
`introduced which just concatenates the name of a column,
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`
`US 6,553,359 B1
`
`7
`the item type in a string representation, and the “stringified”
`value of an item,
`i.e.
`the value of the transaction item.
`Additionally, an optional separator character “@” can be
`used to ensure that the Column and Value function will work
`
`properly. Taking the first record of the data in FIG. 1 as an
`example and applying this mapping function to the State
`column will return “State@CA”. FIG. 5 depicts the com-
`plete result of an application of the introduced mechanism
`for the case where Age, State and Item were selected as item
`columns and the TA column was selected as the transaction
`
`id column. Obviously this is the kind of data which is used
`as input for the search for association rules.
`As a further example for the mapping function the fol-
`lowing mapping would be possible: the values of the items
`of the various item types could be mapped onto non-
`overlapping sub-ranges of a common range with the result
`that encoded transaction items which relate to different item
`
`types are always different with respect
`transaction value.
`
`to the encoded
`
`Advantages of the Invention
`The current invention extends data mining technology
`according to the current state of the art and is now also
`supporting the mining for association rules and/or sequential
`patterns based on data assets comprising items of a multi-
`tude of item types. While current activities in this area of
`technology are concentrating on the search for new and
`advanced mining algorithms the current invention is able to
`achieve this goal by features pointing in a completely
`different and surprising direction. Instead of proposing a
`new mining algorithm the current invention suggests a new
`pre-processing step which transforms the data to be mined
`into a new encoding scheme. The usage of multiple fields to
`be defined as item fields for efficient mining for association/
`sequential patterns is supported without a need to introduce
`a new algorithm because data is not in transaction format.
`Thus mining algorithms proved to be very efficient and
`optimized during the last years are still applicable.
`The current approach is completely general in nature thus
`that no restrictions on the type or semantics of the item fields
`do apply. Therefore state-of-the-art associations and sequen-
`tial pattern algorithms can be applied for mining to arbitrary
`data based on the transformation output of the pre-
`processing step.
`A further characteristics of the proposed teaching case is
`its efficient usage of computer memory. No additional copy-
`ing of data is required and moreover the current approach
`does not need additional disk space being an important point
`in view of the usual large amounts of data to be mined.
`Moreover during the encoding process of the encoded
`transaction items an implicit mapping of item values may be
`possible. The continuous and/or numerical values of the
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`items occurring in the original data assets can be mapped to
`intervals on the fly. Therefore the search for so-called
`quantitative association rules is directly supported.
`The mining output generated by the mining algorithms
`will reflect the column names (as the item type is encoded
`into the transaction item) of the original data schema used as
`input data. This eases the interpretation of the rules/
`sequential patterns significantly.
`What is claimed is:
`
`1. A computerized method of data mining for association
`rules and/or sequential patterns in a multitude of records
`using a data-mining technique that processes transaction-
`records only in transaction format, each said record in said
`multitude of records having a transaction identification and
`at
`least one transaction-item to be data-mined, said
`transaction-item being of a corresponding item-type, said
`multitude of records including transaction-items to be data-
`mined of different item-types, comprising the steps of:
`encoding each transaction-item to be data-mined and its
`corresponding item-type to form an encoded
`transaction-item, said encoded transaction-item con-
`sisting of a single encoded value;
`creating a transaction-record in transaction-format for
`each transaction-item to be data-mined,
`said transaction-record comprising the transaction-
`identification of the record containing said transaction-
`item to be data-mined and said single encoded value;
`and
`
`data-mining said transaction-records for association rules
`and/or sequential patterns using said data-mining tech-
`nique.
`2. Method according to claim 1, wherein said known
`data-mining technique is the “APRIORI” technique.
`3. Method according to claim 1, wherein said encoded
`transaction-item is decodeable into the transaction-item and
`
`its corresponding item-type from which it was formed.
`4. Method according to claim 2, wherein said encoded
`transaction-item is generated by concatenating said item-
`type and said transaction-item.
`5. A system comprising means adapted for carrying out
`the steps of the method according to claim 1.
`6. A data processing program for execution in a data
`processing system comprising software code portions for
`performing a method according to claim 1.
`7. A computer program product stored on a computer
`usable medium, comprising computer readable program
`means for causing a computer to perform a method accord-
`ing to claim 1.