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Electricity market trade based on mobile intelligent device will extend the volume of transaction. For the massive and various trading data, transaction mining algorithm is very useful to find the relationship of correlative elements such as trade price and power capacity, and it always occurs between the power users and power generation enterprises. The novel FP-Table algorithm is proposed in this paper to solve the massive transaction mining problem. The FP-Table algorithm integrates the Hash table into FP-Growth algorithm, using two-dimension table saving frequency count of item pair, then mining the frequency items of electricity transactions efficiently. Application of mobile transaction mining is proved to be high efficiency and high value by performance experiment results.

Electric power trade is the core business of electricity market transaction [

With the application of web 2.0 theory and technology, the mobile device is useful in social network based on internet website; information dissemination is so fast for every online user that mobile technology promotes user involved in the network activity, typical application software such as Facebook, Twitter, Tencent QQ, Dianping, and so on. Not only is the public message pushed to the users, but also the emotion information as product recommendation and dealing preference selection is gotten by the online users. So the application of mobile electricity market based on mobile internet device is high value for power trading users.

As to the massive transaction data mining problem, there has some data mining theory and method research on the business transaction. Agrawal et al. [

The rest of this paper is organized as follows. Section 2 describes the details of the transaction mining based on FP-

Definition 1 The two-dimension transaction table is a data set items associate frequency pattern table, which is made of item rows and item columns, saving the combination count information for a transaction database. A transaction is a collection of multiple items in database. And the arbitrary combination of two items in the transaction is called basic associate frequency pattern unit.

From a transaction of database, a sequence of items is considered as a queue. So the each previous item and the following each item in the item queue constitutes the rows and columns of two-dimensional table, the value between relevant row and column in the table is the frequency count. Since a trade database is composed of many transactions, it also includes all the items of transaction expressing the rows and columns of two-dimen- sional table, showing all the transaction combination counts between every two items in trade database.

For the simple example, a transaction T_{1} is a sequence of items collection {abcef}, the two-dimension table of frequency item pattern count is created in

Definition 2 Hash-T is a hash table, which combines the candidate item frequency pattern with hash table from transaction database.

The candidate items are the whole transaction items in the trade database. Firstly, the database is thoroughly scanned, and the candidate items are counted by frequency, as is called Support degree (Sup). Then all the items are sorted by the sequence. At the same time, the hash table is used for exactly defining candidate items sequence by Sequence Number (SN) and Item Name (Name). The example of Hash-T, from a transaction database including three transaction items, is as

From

Definition 3 FP-

Given

Lemma 1 The item pair in the FP-

Proof Firstly, given a transaction including two items {A_{1}A_{2}}, the frequency item and the frequency count are scanned, noted as 1 A _{2}: 1>; _{}

Then, given two transaction including collection as {A_{1}A_{2}}, {A_{1}A_{2}A_{3}}, the frequency items and the frequency count are also scanned, note as 1 A _{2}: 2>, 1 A _{3}: 1>, 2 A _{3}: 1>, 1 A _{2} A _{3}: 1>. Above frequency count value of combination item 1 A _{2} A _{3}: 1> is 1, just due to the minimal frequency count value of item pair 1 A _{3}: 1> and 2 A _{3}: 1> is 1; _{}

Next, given n transaction including collection as {A_{1}A_{2}}, {A_{1}A_{2}A_{3}}, ∙∙∙, {A_{1}A_{2}A_{3}∙∙∙A_{n}}, the frequency items are scanned and noted as 1 A _{2}: n>, 1 A _{3}: n-1>, ∙∙∙, 1 A _{n}: 1>, 2 A _{3}: n-1>, ∙∙∙, 2 A _{n}: 1>, ∙∙∙, 1 A _{2} A _{3}: n-1>, ∙∙∙, 1 A _{2} A _{3} A _{4}: n-2>, ∙∙∙, 1 A _{2} A _{3} ∙∙∙A _{n} _{-1} A _{n}: 1>. The frequency count value of combination item of 1 A _{2} A _{3} ∙∙∙A _{n} _{-1} A _{n}: 1> is 1, due to the minimal frequency count value of item pair 1 A _{2}: n>, 1 A _{3}: n-1>, 1 A _{4}: n-2>,∙∙∙, 1 A _{n}: 1> is 1. _{}

FP-

Building FP-

Mining FP-

Given transaction mining in electricity market, the transaction database is proposed for electricity transaction as a case study. The items of electricity transaction are derived from mobile electricity trade system platform. Items are classified as power generation trade (a), direct trading between power users and power generation enterprises (b), electricity price (c), inter provincial Trading (d), bilateral negotiation (e), centralized matchmaking (f), medium and long-term transactions (g), month transactions (i), year transactions (j), and delivery of electricity trading (l). The Hash-T table and two-dimension table are built as

For the Hash-T table, all the items are sorted by the support degree (Sup). Then the items which support that degree is less than threshold are removed from Hash-T table. Next, the FP-

From the FP-Table, the frequency item in electricity market is recursively proceeded to pattern mining according to lemma 1. For example, as to item g in sorted Hash-T and FP-Table, the frequency count value between item pairs of g and a is 5, the frequency count value between item pairs of g and c is 5, and the frequency count value between item pairs of g and e is 4. So the frequency item pattern mining process is described as the following: once the frequency count value of g and e is 4, all the frequency count of item combination with e is only 4, that is, the frequency pattern of g has {a g: 5}, {c g: 5}, {e g: 4}, {a c g: 5}, {a e g: 4}, {c e g: 4}, {a c e g: 4} according to the pattern count condition of .

All the items of frequency pattern in electricity trade database are proceeded by the above method. And the experiment proves that the algorithm is efficient and robust.

Gao CC thanks the science and technology research project sponsored by China State Grid Corp under grant number DZN17201400039. The algorithm research and technology application is supported by the project. Then the transaction mining application effect will feed back the experiment in the paper.

Chuncheng Gao,Yong Dai,Minghai Jiao, (2015) Application of Transaction Mining Based on FP-