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代寫termpaper,數據挖掘技術在保險行業中的決策

發布于2020-09-22 作者:留學寫作網 閱讀:
1 Introduction
 
 
With the rapid development of database technology and database management systems widely used, more and more data accumulate all walks of life. Growing surge of data hidden behind a lot of important information that people want to be able to be a higher level of analysis in order to make better use of the data. The current database systems can efficiently implement data entry, query, statistics and other functions, but can not find the data relationships and rules exist, can not be based on existing data to predict future trends. Lack of knowledge hidden behind data mining tools, led to the "data explosion but knowledge poor" phenomenon.
 
 
With the development of computer and network technology, access to a particular industry relevant information has been feasible. For large quantities, involving a wide range of data, relying on the traditional simple summary of the specified model to analyze the statistical methods of data analysis can not be completed. Therefore, an intelligent analysis of information technology - "data mining" (Data Mining) came into being.
 
 
Data Mining (Data Mining) is a large, incomplete, noisy, fuzzy, random data to extract implicit in them, people are not known in advance, but is potentially useful information and knowledge in the process . By mining data warehouse to store large amounts of data, and found a new association meaningful patterns and trends in the process. Data mining is a new business information processing technology, is a large number of commercial database business data extraction, transformation, analysis and processing of other models to extract critical data supporting business decisions. So that enterprises in the fierce market competition opportunities. As for the insurance industry, currently has a broad market demand.
 
2 Item Description
The project has developed "the insurance industry decision system V1.0". The main interface of system operation using ASP programming: data preprocessing, customers to buy insurance analysis, customer buying habits analysis and the results output functions; background database using the Sql Server 2005 network database implementation; mining tools using SPSS Clementine 11.0; experiments in the study stage Apriori algorithm exists for "Storage complexity" and "a lot of redundant rules," two major drawbacks of the algorithm to improve through the use of a pattern tree structure to reduce the complexity of storage Apriori algorithm, while reducing the appearance of redundant rules .
The system consists of: data preprocessing, customers to buy insurance analysis, customer buying habits analysis and the results output and other major functional blocks.
 
 
(1) "preprocessing" modules include: upload, data platform, data processing, statistics, and other functions to generate data sets.
● Upload: to be completed by all branches Insurance Corporation under the data upload.
● Data Platform: allows the data before uploading data platform to choose.
● Data processing: cleaning up the data, format conversion and other operations.
● Statistics: The preprocessed data analysis, extraction efficacy data.
● generate data sets: the statistical data generating process to extract the active data set, to provide a higher quality data mining data source.
 
 
(2) "customers to buy insurance analysis" modules include: data import, parameter setting, result analysis and other functions.
● Data Import: In this user interface, by selecting different data platform will go through "data preprocessing" generated data sets were imported.
● Parameter setting: In this user interface settings "support", "confidence" and other parameters for effective analysis of the data set with the value range of the data record filter.
● Analysis: In this user interface can be "customers to buy insurance analysis," the final results of the analysis to the "report", "chart" format display, the results of this analysis for the industry to provide a "same customer buy our various (sub) insurance "customer information, thus providing the industry" to win customers' decision-making basis.
 
 
(3) "customer buying habits of" modules include: data import, parameter setting, result analysis and other functions.
● Data Import: This operation is the same (2) "customers to buy insurance analysis" module "Data Import."
● Parameter setting: In this setting, respectively, "Input Parameters" (including: age, gender, occupation and other basic customer information) and "Output Parameters" (customers buy insurance information).
● Analysis: With this interface can demonstrate customer buying habits analysis, thus providing the industry "to retain customers' decision-making basis.
(4) "analysis result output" modules include: "Analysis of customers to buy insurance" and "customer buying habits analysis" of the print output results.
 
Three projects improved fast algorithm
Since Apriori algorithm time and space complexity is high and there is a large amount of redundant rules two major defects. Therefore, this project through the use of a pattern tree structure to reduce the complexity of storage Apriori algorithm, while reducing redundant rules appear.
 
 
3.1 a pattern tree structure
root is the one labeled as "null" the root, root root following the child's program as a prefix sub-tree collection, as well as project head table composition; tree each node contains four fields user_id, count, node_link, node_next. Which, user_id is user tags (uniquely identifies a user), count for the parent node of the node reaches the number of paths, node_link point to the same tree the user_id next node to the next node, the moment a node does not exist, node_link is null, node_next pointing to its child nodes in the tree; program header table for each table entry contains three fields: user_id, count, head of node, user_id with the same meaning as defined in the tree, count as user_id of the tree and all the same, head of node points to the tree with the same user_id value of the first node pointer.
 
 
3.2 Creating Pattern Tree
Algorithm is as follows:
Let the transaction database as A, one of the items set to Ai.
Algorithm: Patterntree (tree, p), constructed pattern tree
Input: A transaction database user
Output: User mode tree
Procedure Patterntree (T, p)
{Create_ tree (T) ;/ / create a Pattern-Tree root node to "null" mark
t = T; / / t for the current node
While A <> null do
{Read into a transactional database item set Ai
while p! = null
do
{If p.user_id == t ancestors n.user_id
then
{N.count = n.count + l;
t = n;
}
Elseif p.user_id == T kids c.user_id
then
{C.count = c.count + l;
t = c;
}
else
insert_Patterntree (T, p) ;/ / put p as a new node into the tree, as the current node's child nodes
p = p.next;
}
}
}
 
 
3.3 pairs pattern tree pruning
Pattern tree is established, there may be a large number of redundant branches, in order to ensure that the data mining results will not be the redundant branches affected by the noise generated, so the need for tree pruning, removing noise information.
Algorithm: SPT (Tree, a), by calling the model tree pruning algorithm
/ / SPT to support pattern tree, ie Supported Access Pattern Tree; a head table for the project
Input: Pattern tree PatternTree, Min_Sup (Pattern Tree minimum support)
Output: After pruning the support pattern tree SPT, mode B = {bi | i = 1,2,3 ...... n}
SPT (Tree, a)
{I = 1;
While (ai! = null) / / for the project head table in a one
{
if (ai.count> = Min_Sup)
then
{
Mode bi = ai.head of node;
p = ai.head of node ;/ / p in the schema tree pointing ai
Location
While (p! = null and ai.count> = Min_Sup)
{
Find the prefix p group, the p-group, and p connection prefix, configuration
Into Mode b;
if (bi.count> = Min_Sup)
then
{
/ / Bi.count the mode p and p b is the base of the prefix
The minimum count
P in the schema bi retain their prefixes base;
bi = bi. node_link
}
else
{
Depending on the mode of p and b prefix base deletion
PatternTree the corresponding node, a child node reconfiguration
With the parent node, and modify the project header table ai;
p = p. node_next / / p points in the pattern tree
Next position;   
}
}
}
else
{
Modify the project head node ai value;
Delete mode corresponding node in the tree and prefix-based, reconstruction Sons
Node;
i + +;
}
}
}
 
 
The establishment of the tree can be avoided through mode multiple scans the transaction database; while taking advantage count field effectively retains the number of itemsets to avoid generating a large number of frequent itemsets, for reducing the complexity of space-time has played a certain role. Tree structure can be avoided through a large amount of redundant rules.
Through the pattern tree pruning, tree can be deducted in the pattern generation process produces a large number of redundant branches, played a role in reducing the space complexity, and can utilize the output mode B production rules, to avoid a number of sets appears frequently, reducing the time complexity.
 
4 Conclusion
The project tree structure by mode improved Apriori algorithm, Apriori algorithm to make up for the defects. This method is not only capable of Apriori algorithm from time complexity and space complexity to improve on, while avoiding the generation of intermediate rules. This study shows that by using a pattern tree structure to reduce the complexity of storage Apriori algorithm, while reducing the appearance of redundant rules, which improved Apriori algorithm is an effective measure.
1引言
 
 
隨著數據庫技術和數據庫管理系統中廣泛應用的飛速發展,越來越多的數據積累各行各業。日益激增的數據背后隱藏了很多重要的信息,人們希望能夠是一個更高層次的分析,以更好地利用數據。目前的數據庫系統可以有效地實現數據的錄入,查詢,統計等功能,但無法找到數據的關系和規則的存在,無法根據現有的數據預測未來的發展趨勢。背后隱藏的數據挖掘工具,知識的缺乏導致“數據爆炸但知識差”的現象。
 
 
隨著計算機和網絡技術的發展,進入特定行業相關信息一直是可行的。對于數量大,涉及范圍廣,數據,分析統計的數據分析方法,不能完成指定的模型依賴于傳統的簡單總結。因此,信息技術 - 智能分析“數據挖掘”(數據挖掘)應運而生。
 
 
數據挖掘(數據挖掘)是一個大型的,不完整的,嘈雜的,模糊的,隨機的數據中提取隱含在他們的,人們事先不知道的,但在這個過程中是潛在有用的信息和知識。通過挖掘數據倉庫存儲大量的數據,并發現了一個新的關聯有意義的模式和趨勢的過程中。數據挖掘是一種新的商業信息處理技術,大量的商業數據庫數據抽取,轉換,分析和處理的其他車型中提取關鍵數據,支持業務決策。使企業在激烈的市場競爭機會。至于保險業,目前擁有廣闊的市場需求。
 
2項目
該項目開發了“保險業決策系統V1.0”。使用ASP編程系統操作的主界面:數據預處理,客戶買保險分析,顧客的購買習慣分析和結果輸出功能,后臺數據庫采用SQL Server 2005中的網絡數據庫實現,采礦工具,使用SPSS Clementine的11.0;實驗Apriori算法存在研究階段“存儲復雜性”,“很多冗余的規則,”兩大弊端的算法,通過使用模式樹結構,以減少存儲Apriori算法的復雜性提高,同時減少了冗余的外觀規則。
該系統包括:數據預處理,客戶購買保險的分析,顧客的購買習慣分析,分析結果輸出等主要功能模塊。
 
 
(1)“預處理”模塊包括:上傳,數據平臺,數據處理,統計,生成數據集等功能。
●上傳:可完成所有分支機構的保險公司下的數據上傳。
●數據平臺:允許上傳數據平臺的數據,然后再選擇。
●數據處理:清理數據,格式轉換等操作。
●統計:預處理后的數據分析,提取療效數據。
●生成數據集的統計數據生成處理,以提取有效的數據集,以提供更高質量的數據挖掘的數據源。
 
 
(2)“客戶買保險分析”模塊包括:數據導入,參數設置,結果分析等功能。
●數據導入:在這個用戶界面,通過選擇不同的數據平臺將通過“數據預處理”生成數據集進口。
●參數設置:在用戶界面設置“支持”,“信心”和其他參數的設定值范圍的數據記錄過濾器的數據進行有效的分析。
●分析:在這個用戶界面可以是“客戶買保險分析,”最后結果的分析“報告”,“圖”的格式顯示,這個分析結果,為行業提供了“同一個客戶買我們的各個(子)保險“的客戶信息,從而提供了業界”贏得了客戶的決策依據。
 
 
(3)“顧客的購買習慣”模塊包括:數據導入,參數設置,結果分析等功能。
●數據導入:這個操作是相同的(2)“客戶買保險分析”模塊“數據導入”。
●參數設置:在此設置,分別為“輸入參數”(包括:年齡,性別,職業等基本的客戶信息)和“輸出參數”(客戶購買保險的信息)。
●分析:有了這個接口,可以證明顧客的購買習慣分析,從而提供業界“保留客戶的決策的基礎。
(4)“分析結果輸出”模塊包括:“客戶買保險”和“顧客的購買習慣分析”的打印輸出結果的分析。
 
三個項目提高快速算法
由于Apriori算法的時間和空間復雜度為高,并有大量冗余規則兩大缺陷。因此,該項目通過使用模式樹結構,以減少存儲Apriori算法的復雜性,同時減少冗余規則出現。
 
 
3.1的模式樹結構
根是一個標記為“空”的根,根孩子的程序作為前綴子樹集合,以及項目表頭組成,樹的每個節點包含四個領域的user_id,計數,node_link node_next。其中,user_id是用戶標簽(唯一標識用戶),計數的節點的父節點的路徑的數量達到相同的樹的節點不存在的時刻到下一個節點的下一個節點的user_id,,node_link點, node_link為null,node_next指向其子樹中的節點;程序頭表每個表項包含三個字段:user_id的,計數,user_id的頭節點,在樹的定義具有相同含義,算作user_id為樹都一樣,頭結點user_id的值具有相同的樹的第一個節點的指針。
 
 
3.2創建模式樹
算法如下:
讓交易數據庫為A,艾設定的項目之一。
算法:Patterntree(樹,P),建造模式樹
輸入:事務數據庫用戶
輸出模式:用戶模式樹
步驟Patterntree(T,P)
{CREATE_樹(T);/ /創建一個模式樹的根節點到“空”的標志
T = T; / /噸,目前的節點
當A <>空
{讀入數據庫項目設置艾事務
而P! = NULL
{如果p.user_id ==噸祖先的n.user_id
然后
{N.count = n.count +升;
T = N;
}
ELSEIF p.user_id == T孩子c.user_id
然后
{C.count = c.count +升;
T = C;
}
其他
insert_Patterntree(T,P);/ /把p作為一個新的節點到樹上,作為當前節點的子節點
P = p.next;
}
}
}
 
 
3.3雙模式樹的修剪
模式樹的建立,有可能會出現大量的冗余分支,以確保數據挖掘結果將不影響所產生的噪音,所以需要修剪樹木,去除噪聲的信息的冗余分支。
算法:SPT(樹),通過調用模型樹剪枝算法
/ /模式樹SPT支持,即支持的訪問模式樹頭表項目
輸入:的模式樹PatternTree,Min_Sup(最小模式樹的支持)
輸出:修剪后的支持模式樹SPT,模式B = {雙向| I = 1,2,3 ...... n}的
SPT(樹,A)
{i = 1;
(ai! = NULL)/ /在一個項目頭表
{
(ai.count> = Min_Sup)
然后
{
模式的雙向= ai.head節點;
P = ai.head ;/ / p的架構樹節點指向AI
位置
(p! = null和ai.count> = Min_Sup)
{
搜尋p-群的前綴p基團,和p連接前綴,配置
到模式B;
(bi.count> = Min_Sup)
然后
{
/ / Bi.count模式p和pb是基的前綴
的最小計數
P在架構雙向保留其前綴基地;
BI =雙向。 node_link
}
其他
{
根據P和B模式的前綴堿基缺失
PatternTree相應的節點,子節點的重新配置
與父節點,并修改項目頭表AI;
P = P。在模式樹的node_next / / p指向
下一個位置;
}
}
}
其他
{
修改項目的頭節點AI值;
刪除模式對應的節點在樹中前綴為基礎,重建父子
節點;
i + +;
}
}
}
 
 
樹的建立可以通過模式避免多個掃描事務數據庫,同時有效利用計數字段保留項集數,以避免產生大量的頻繁項集,減少時間和空間的復雜性,起到了一定的作用。樹結構中,可避免通過大量的冗余規則。
通過模式樹修剪,樹可以扣模式生成過程中產生了大量的多余的樹枝,發揮了作用,減少了空間的復雜性,可以利用輸出模式B生產規則,避免了多套頻繁出現,降低了時間復雜度。
 
4結論
該項目的樹狀結構模式改進的Apriori算法,Apriori算法來彌補缺陷。此方法不僅能夠Apriori算法的時間復雜度和空間復雜度,以改善,同時也避免了產生中間規則。這項研究表明,通過使用模式樹結構,減少存儲Apriori算法的復雜性,同時減少了冗余的規則,提高Apriori算法的外觀是一個有效的措施。

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