Mining Allocating Patterns in Investment Portfolios

2009 ◽  
pp. 110-135
Author(s):  
Yanbo J. Wang ◽  
Xinwei Zheng ◽  
Frans Coenen
Keyword(s):  
Data Mining ◽  
Portfolio Management ◽  
Association Rule ◽  
Common Type ◽  
Experimental Results ◽  
Transaction Database ◽  
Investment Portfolios ◽  
The Given

An association rule (AR) is a common type of mined knowledge in data mining that describes an implicative co-occurring relationship between two sets of binary-valued transaction-database attributes, expressed in the form of an ? rule. A variation of ARs is the (WARs), which addresses the weighting issue in ARs. In this chapter, the authors introduce the concept of “one-sum” WAR and name such WARs as allocating patterns (ALPs). An algorithm is proposed to extract hidden and interesting ALPs from data. The authors further indicate that ALPs can be applied in portfolio management. Firstly by modelling a collection of investment portfolios as a one-sum weighted transaction- database that contains hidden ALPs. Secondly the authors show that ALPs, mined from the given portfolio-data, can be applied to guide future investment activities. The experimental results show good performance that demonstrates the effectiveness of using ALPs in the proposed application.

Mining Allocating Patterns in Investment Portfolios

2009 ◽  
pp. 2657-2684
Author(s):  
Yanbo J. Wang ◽  
Xinwei Zheng ◽  
Frans Coenen
Keyword(s):  
Data Mining ◽  
Portfolio Management ◽  
Association Rule ◽  
Common Type ◽  
Experimental Results ◽  
Transaction Database ◽  
Investment Portfolios ◽  
The Given

An association rule (AR) is a common type of mined knowledge in data mining that describes an implicative co-occurring relationship between two sets of binary-valued transaction-database attributes, expressed in the form of an ? rule. A variation of ARs is the (WARs), which addresses the weighting issue in ARs. In this chapter, the authors introduce the concept of “one-sum” WAR and name such WARs as allocating patterns (ALPs). An algorithm is proposed to extract hidden and interesting ALPs from data. The authors further indicate that ALPs can be applied in portfolio management. Firstly by modelling a collection of investment portfolios as a one-sum weighted transaction-database that contains hidden ALPs. Secondly the authors show that ALPs, mined from the given portfolio-data, can be applied to guide future investment activities. The experimental results show good performance that demonstrates the effectiveness of using ALPs in the proposed application.

scholarly journals Present State-of-The-Art of Association Rule Mining Algorithms

2019 ◽  
Vol 9 (1) ◽  
pp. 6398-6405
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
State Of The Art ◽  
Synthetic Data ◽  
Data Sets ◽  
Evolutionary Analysis ◽  
Rule Mining ◽  
Transaction Database ◽  
Mining Algorithms

A Data mining is the method of extracting useful information from various repositories such as Relational Database, Transaction database, spatial database, Temporal and Time-series database, Data Warehouses, World Wide Web. Various functionalities of Data mining include Characterization and Discrimination, Classification and prediction, Association Rule Mining, Cluster analysis, Evolutionary analysis. Association Rule mining is one of the most important techniques of Data Mining, that aims at extracting interesting relationships within the data. In this paper we study various Association Rule mining algorithms, also compare them by using synthetic data sets, and we provide the results obtained from the experimental analysis

A Novel and Improved Apriori Algorithm

2014 ◽  
Vol 721 ◽  
pp. 543-546 ◽  
Author(s):  
Dong Juan Gu ◽  
Lei Xia
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Inner Product ◽  
Apriori Algorithm ◽  
Product Operation ◽  
Classical Algorithm ◽  
Transaction Database ◽  
The Given ◽  
To Receive ◽  
Improved Algorithm

Apriori algorithm is the classical algorithm in data mining association rules. Because the Apriori algorithm needs scan database for many times, it runs too slowly. In order to improve the running efficiency, this paper improves the Apriori algorithm based on the Apriori analysis. The improved idea is that it transforms the transaction database into corresponding 0-1 matrix. Whose each vector and subsequent vector does inner product operation to receive support. And comparing with the given minsupport, the rows and columns will be deleted if vector are less than the minsupport, so as to reduce the size of the rating matrix, improve the running speeding. Because the improved algorithm only needs to scan the database once when running, therefore the running speeding is more quickly. The experiment also shows that this improved algorithm is efficient and feasible.

Association Rule Mining

2011 ◽  
pp. 172-178
Author(s):  
Vasudha Bhatnagar ◽  
Sarabjeet Kochhar
Keyword(s):  
Data Mining ◽  
Portfolio Management ◽  
Association Rule ◽  
Association Rule Mining ◽  
Process Management ◽  
Rule Mining ◽  
Data Mining Techniques ◽  
Future Data ◽  
Manufacturing Process Management ◽  
Care Improvement

Data mining is a field encompassing study of the tools and techniques to assist humans in intelligently analyzing (mining) mountains of data. Data mining has found successful applications in many fields including sales and marketing, financial crime identification, portfolio management, medical diagnosis, manufacturing process management and health care improvement etc.. Data mining techniques can be classified as either descriptive or predictive techniques. Descriptive techniques summarize / characterize general properties of data, while predictive techniques construct a model from the historical data and use it to predict some characteristics of the future data. Association rule mining, sequence analysis and clustering are key descriptive data mining techniques, while classification and regression are predictive techniques. The objective of this article is to introduce the problem of association rule mining and describe some approaches to solve the problem.

Reasoning about Frequent Patterns with Negation

2011 ◽  
pp. 941-946 ◽  
Author(s):  
Marzena Kryszkiewicz
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
White Wine ◽  
Frequent Patterns ◽  
Sales Managers ◽  
Important Data ◽  
Large Databases ◽  
Transaction Database ◽  
Significant Patterns

Discovering frequent patterns in large databases is an important data mining problem. The problem was introduced in (Agrawal, Imielinski, & Swami, 1993) for a sales transaction database. Frequent patterns were defined there as sets of items that are purchased together frequently. Frequent patterns are commonly used for building association rules. For example, an association rule may state that 80% of customers who buy fish also buy white wine. This rule is derivable from the fact that fish occurs in 5% of sales transactions and set {fish, white wine} occurs in 4% of transactions. Patterns and association rules can be generalized by admitting negation. A sample association rule with negation could state that 75% of customers who buy coke also buy chips and neither beer nor milk. The knowledge of this kind is important not only for sales managers, but also in medical areas (Tsumoto, 2002). Admitting negation in patterns usually results in an abundance of mined patterns, which makes analysis of the discovered knowledge infeasible. It is thus preferable to discover and store a possibly small fraction of patterns, from which one can derive all other significant patterns when required. In this chapter, we introduce first lossless representations of frequent patterns with negation.

Sensitivity Association Rule Mining using Weight based Fuzzy Logic

2017 ◽  
Vol 9 (2) ◽  
pp. 1 ◽  
Author(s):  
Meenakshi Bansal ◽  
Dinesh Grover ◽  
Dhiraj Sharma
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Side Effects ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Privacy Preserving ◽  
Experimental Results ◽  
Rule Mining

Mining of sensitive rules is the most important task in data mining. Most of the existing techniques worked on finding sensitive rules based upon the crisp thresh hold value of support and confidence which cause serious side effects to the original database. To avoid these crisp boundaries this paper aims to use WFPPM (Weighted Fuzzy Privacy Preserving Mining) to extract sensitive association rules. WFPPM completely find the sensitive rules by calculating the weights of the rules. At first, we apply FP-Growth to mine association rules from the database. Next, we implement fuzzy to find the sensitive rules among the extracted rules. Experimental results show that the proposed scheme find actual sensitive rules without any modification along with maintaining the quality of the released data as compared to the previous techniques.

scholarly journals Generating Non-redundant Multilevel Association Rules Using Min-max Exact Rules

2018 ◽  
Vol 8 (6) ◽  
pp. 4568
Author(s):  
R. Vijaya Prakash ◽  
S. S. V. N. Sarma ◽  
M. Sheshikala
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Huge Number ◽  
Concise Representation ◽  
Multi Level ◽  
The Given ◽  
Multilevel Association Rules

Association Rule mining plays an important role in the discovery of knowledge and information. Association Rule mining discovers huge number of rules for any dataset for different support and confidence values, among this many of them are redundant, especially in the case of multi-level datasets. Mining non-redundant Association Rules in multi-level dataset is a big concern in field of Data mining. In this paper, we present a definition for redundancy and a concise representation called Reliable Exact basis for representing non-redundant Association Rules from multi-level datasets. The given non-redundant Association Rules are loss less representation for any datasets.

Reasoning about Frequent Patterns with Negation

2011 ◽  
pp. 1667-1674
Author(s):  
Marzena Kryszkiewicz
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
White Wine ◽  
Frequent Patterns ◽  
Sales Managers ◽  
Important Data ◽  
Large Databases ◽  
Transaction Database ◽  
Significant Patterns

Discovering of frequent patterns in large databases is an important data mining problem. The problem was introduced in (Agrawal, Imielinski & Swami, 1993) for a sales transaction database. Frequent patterns were defined there as sets of items that are purchased together frequently. Frequent patterns are commonly used for building association rules. For example, an association rule may state that 80% of customers who buy fish also buy white wine. This rule is derivable from the fact that fish occurs in 5% of sales transactions and set {fish, white wine} occurs in 4% of transactions. Patterns and association rules can be generalized by admitting negation. A sample association rule with negation could state that 75% of customers who buy coke also buy chips and neither beer nor milk. The knowledge of this kind is important not only for sales managers, but also in medical areas (Tsumoto, 2002). Admitting negation in patterns usually results in an abundance of mined patterns, which makes analysis of the discovered knowledge infeasible. It is thus preferable to discover and store a possibly small fraction of patterns, from which one can derive all other significant patterns when required. In this chapter, we introduce first lossless representations of frequent patterns with negation.

scholarly journals IMPLEMENTASI DATA MINING PADA PENJUALAN PRODUK DI CV CAHAYA SETYA MENGGUNAKAN ALGORITMA FP-GROWTH

Petir ◽  
2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Wahyu Nur Setyo ◽  
Sukma Wardhana
Keyword(s):  
Data Mining ◽  
Large Volume ◽  
Association Rule ◽  
Computer Systems ◽  
Frequent Pattern ◽  
Retail Industry ◽  
Transaction Data ◽  
Pattern Growth ◽  
Transaction Database ◽  
Mining Association Rule

At this time the growth of data occurs rapidly and rapidly along with the use of computer systems in various transactions. But this increasingly large volume of data has no meaning if it is not processed into a knowledge where this is done by data mining. Association rule or what is known as market based analysis is one type of data mining implementation. This study aims to find patterns of transaction data in the CV Cahaya Setya retail industry by using an Frequent Pattern Growth algorithm or also known as FP-Growth algorithm. FP-Growth aims to find all the set items that can be retrieved (often found) from the transaction database as efficiently as possible. The results of this study show that the pattern on the database of consumer transactions at CV Cahaya Setya retail industry is can be found using the FP-Growth algorithm then implementing it in the application.

scholarly journals Penerapan Metode Association Rule Dalam Menganalisa Data Penjualan Obat Mengunakan Algoritma FP-GROWTH (Studi Kasus Rumah Sakit Haji Medan)

2019 ◽  
Vol 3 (2) ◽  
pp. 9
Author(s):  
Ibnu Rusydi
Keyword(s):  
Data Mining ◽  
Data Structure ◽  
Association Rule ◽  
Hospital Pharmacy ◽  
Business Processes ◽  
Frequent Itemset ◽  
Transaction Data ◽  
New Knowledge ◽  
Transaction Database ◽  
Tree Development

<p><em>Sales transaction data is a very valuable asset in business processes. Not only is it used to calculate profits and money, but large amounts of transaction data can also be used for various purposes to generate new knowledge (knowledge) in the transaction database. Ways that can be done for data processing and generate new knowledge from the data is to use data mining techniques. The technique used in this case is the FP-Growth Algorithm. The data structure used is a tree called FP-Tree. By using FP-Tree, FP-growth Algorithm can directly extract Itemset from FP-Tree. Research conducted by collecting data related to research in the case studio at Medan Haji Hospital Pharmacy where the variables taken are daily drug transaction data. The results of this study are part of the new knowledge of this sales data by applying the FP-Growth Algorithm that uses the concept of FP-Tree development in finding Frequent Itemset that is useful for the development of investment plans in the study areas taken.</em></p><p><em> </em></p><p><em>Keywords: Data Mining, Association Rules, Frequent Itemset, FP-Growth.</em></p>

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