Development of intelligent hybrid learning system using clustering and knowledge-based neural networks for economic forecasting : First phase

The economic forecasting environment is currently undergoing drastic changes and has a complex and challenging task.Practically, people design a database application or use a statistical package to conduct the analysis on the data.Former approach can be done on the online data, but it must be developed after stating the goal of analysis, which means it only possible for a limited and specific purpose.Whereas the statistical approach must be done for the offline data, however it can lead to the missing pattern and undiscovered knowledge from the available data (Shan, C., 1998).For the effort to extract implicit, previously unknown, hidden and potentially useful information from raw data in an automatic fashion, leads us to the usage of data mining technique that receives big attention from the researchers recently.This paper proposed the issues of joint clustering and knowledge-based neural networks techniques as the application for point forecast decision making.Future prediction (e.g., political condition, corporation factors, macro economy factors, and psychological factors of investors) perform an important rule in Stock Exchange, so in our prediction model we will be able to predict results more precisely. We proposed KMeans clustering algorithm that is based on multidimensional scaling, joined with neural knowledge based technique algorithm for supporting the learning module to generate interesting clusters that will generate interesting rules for extracting knowledge from stock exchange databases efficiently and accurately

An initial state of design and development of intelligent knowledge discovery system for stock exchange database

Data mining is a challenging matter in research field for the last few years.Researchers are using different techniques in data mining.This paper discussed the initial state of Design and Development Intelligent Knowledge Discovery System for Stock Exchange (SE) Databases. We divide our problem in two modules.In first module we define Fuzzy Rule Base System to determined vague information in stock exchange databases.After normalizing massive amount of data we will apply our proposed approach, Mining Frequent Patterns with Neural Networks.Future prediction (e.g., political condition, corporation factors, macro economy factors, and psychological factors of investors) perform an important rule in Stock Exchange, so in our prediction model we will be able to predict results more precisely.In second module we will generate clustering algorithm. Generally our clustering algorithm consists of two steps including training and running steps.The training step is conducted for generating the neural network knowledge based on clustering.In running step, neural network knowledge based is used for supporting the Module in order to generate learned complete data, transformed data and interesting clusters that will help to generate interesting rules

Clustering technique in data mining : general and research perspective.

As the amount and dimensionality of data grows beyond the grasp of human minds, automation of pattern discovery becomes crucial. One of the most popular techniques to extract pattern and knowledge from large amount of data in databases is data mining. Data mining can be defined as process of searching the particular patterns and relationship from large amount of data in databases using sophisticated data analysis tools and techniques to build models that may be used to make valid predictions. One of the existing data mining techniques is clustering. Clustering in data mining is a discovery process that groups a set of data such that the intra-cluster similarity is maximized and inter-cluster similarity is minimizes. These discovered clusters are used to explain the characteristics of the data distribution. This paper present most popular clustering technique such as hierarchical clustering and partitional clustering, cluster selection schemes, clustering criterion functions, assessing cluster quality and conclusion

Design and development of intelligent knowledge discovery system for stock exchange database

The stock market is a complex, nonstationary, chaotic and non-linear dynamical system. Most of the existing methods suffer from drawbacks like long training times required, often hard to understand results, and inaccurate predictions. This study focuses on data mining approach for stock market prediction. The aim is to discover unknown patterns, new rules and hidden knowledge from large databases of stock index that are potentially useful and ultimately understandable for making crucial decisions related to stock market. The prototype knowledge discovery system developed in this research can produce accurate and effective information in order to facilitate economic activities. The developed prototype consists of mainly two parts: i) based on Fuzzy decision tree (FDT); and ii) based on support vector regression (SVR). In predictive FDT, aim is to combine the symbolic decision trees with approximate reasoning offered by fuzzy representation. In fuzzy reasoning method, the weights are assigned to each proposition in the antecedent part and the Certainty Factor (CF) is computed for the consequent part of each Fuzzy Production Rule (FPR). Then for stock market prediction significant weighted fuzzy production rules (WFPRs) are extracted. The predictive FDTs are tested using three data sets including Kuala Lumpur Stock Exchange (KLSE), New York Stock Exchange (NYSE) and London Stock Exchange (LSE). The results of predictive FDT method are favorably compared with those of other random walk models like Autoregression Moving Average (ARMA) and Autoregression Integrated Moving Average (ARIMA). The SVR prediction system is based on support vector machine (SVM) approach. Weighted kernel based clustering method with neighborhood constraints is incorporated in this system for getting improved prediction results. The SVM based method gives better results than backpropagation neural networks. SVM offers the advantages including: i) there is a smaller number of free parameters; ii) SVM forecasts better as it offers better generalization; iii) training SVM is faster. In essence, both the subsystems (FDT and SVR based) developed in this project are complementary to each other. As the fuzzy decision tree based system gives easily interpretable results, we mainly use it to classify past and present data records. Whereas we use the stronger aspect of the SVR based approach for prediction of future trend of the stock market, and get improved results

Kecenderungan ibubapa muslim terhadap pemakanan halal: kajian di Skudai, Johor Bahru Johor

Setahun lalu kerajaan mengenalpasti perdagangan makanan ‘halal’ sebagai satu matlamat dalam Dasar Pertanian Negara yang Ketiga (1998-2010). Malaysia juga sedang berusaha menjadi pusat pentauliahan dan standard serta menjalankan penghantaran dan pengeluaran makanan halal. Kredibiliti Malaysia diakui dunia di mana Suruhanjaya Alimentaris Kodeks Bangsa-Bangsa Bersatu (yang bertanggungjawab memperkenlkan Progr am Standard Makanan FO/WHO) menyebut Malaysia sebagai contoh terbaik pada peringkat dunia bagi makanan halal. Walaubagimanapun realitinya masyarakat Islam di Malaysia masih kurang prihatin. Di negara kita tidak semua produk dicatatkan isi 3 kandungannya. Jika berlaku penipuan dalam menjaga kesucian makanan yang dijual, pihak berkuasa tentu memakan masa untuk menyelesaikanny