Multidimensional Time Series Fuzzy Association Rules Mining

In this paper, we present a new solution, in which the fuzziness of both subsequences and subsequences interval has been taken into consideration for solving the problem of multidimensional time series fuzzy association rules mining. Aimed at dealing with the new conception, this paper has put forward some key algorithms of the solution. Finally, an application example of multidimensional time series fuzzy association rules mining is illustrated. The result shows that rules with fuzzy interval can only be mined out by the above-mentioned new method

Studies on Ontology Meta-Model for Isomorphic Architecture of Information Systems based on Organizational Semiotics

Interoperability is hard to tackle in both business and IT domains since semantic transaction loss exists in terms of concepts transformation from one design stage to another in information systems development. It results from different interpretations and representations of various requirements in design domains. Without an explicit structural specification of semantic linkages among design domains, the transformation cannot be efficiently identified in an appropriate way. These call for effective architectural solutions that coordinate powerful technologies with business applications to enable seamless integration. The main objective of this paper is to investigate ontology types and build ontology meta-model for IAIS (Isomorphic Architecture of Information Systems) which was built in our previous work to reach seamless and unified semantic linkages. The ontology meta-model is proposed to bridge the gap among different processes in information systems development with the same structure unit. The secondary objective of this paper is to study how to prevent semantic loss in analysis and design processes with the meta-model

A Grey Model-Least Squares Support Vector Machine Method for Time Series Prediction

In this study, the authors aim to solve the time series prediction problem through pre-predicting multiple influence factors of the target sequence. Focusing on two pre-prediction approaches of influence factors (i.e., factors driven approach and time driven approach), we propose a time series prediction method based on the least squares support vector machine and grey model (GM-LSSVM). This method could improve the prediction precision of the target time series by differentiating the data characteristics of influence factors. A case study is put forward to predict China\u27s economy from the perspective of system innovation and technological innovation. We selected public statistics data from 2005 to 2014 from the national bureau. The numerical experiment results illustrate that the accuracy of the GM-LSSVM is able to reach 95%, which proves the effectiveness of our proposed method in practice

A New Time Series Similarity Measurement Method Based on Fluctuation Features

Time series similarity measurement is one of the fundamental tasks in time series data mining, and there are many studies on time series similarity measurement methods. However, the majority of them only calculate the distance between equal-length time series, and also cannot adequately reflect the fluctuation features of time series. To solve this problem, a new time series similarity measurement method based on fluctuation features is proposed in this paper. Firstly, the fluctuation features extraction method of time series is introduced. By defining and identifying fluctuation points, the fluctuation points sequence is obtained to represent the original time series for subsequent analysis. Then, a new similarity measurement (D_SM) is put forward to calculate the distance between different fluctuation points sequences. This method can calculate the distance of unequal-length time series, and it includes two main steps: similarity matching and the distance calculation based on similarity matching. Finally, the experiments are performed on some public time series using agglomerative hierarchical clustering based on D_SM. Compared to some traditional time series similarity measurements, the clustering results show that the proposed method can effectively distinguish time series with similar shapes from different classes and get a visible improvement in clustering accuracy in terms of F-Measure

Multifunctional Product Marketing Using Social Media Based on the Variable-Scale Clustering

Customers\u27 demands have become more dynamic and complicated owing to the functional diversity and lifecycle reduction of products which pushes enterprises to identify the real-time needs of distinct customers in a superior way. Meanwhile, social media turned as an emerging channel where customers often spontaneously can express their perceptions and thoughts about products promptly. This paper examines the customer satisfaction identification and improvement problem based on social media mining. First, we proposed the public opinion sensitivity index (POSI) to uncover target customers from extensive short-textual reviews. Subsequently, we presented a customer segmentation approach based on the sentiment analysis and the variable-scale clustering (VSC). The approach is able to get several customer clusters with the same satisfaction level where customers belonging to each cluster have similar interests. Finally, customer-centered marketing strategies and customer difference marketing campaigns are planned under the shadow of customer segmentation results. The experiments illustrate that our proposed method can support marketing decision marketing in practice that enriches the intention of the current customer relationship management

Construction Method of Tender Document Based on Case-based Reasoning

The core activities of tender documents compilation are to collect similar historical tender documents, select compilation templates of tender documents and revise templates of tender documents partially. However, when the historical tender documents have accumulated to a certain amount, it becomes extremely difficult for compilers to summary, reuse and revise templates artificially in traditional compiling methods. Based on casebased reasoning (CBR), this paper studied the content recommendation method in the process of tender document construction. Firstly, a structured model of tender documents was constructed, and similar tender cases were retrieved from the tender case database according to the characteristics of tender cases; Secondly, the non-interference sequence index was used to measure the similarity of clauses used in similar tender cases, and the recommended sequences of reference template and content module of tender documents were constructed, which realized the recommendation of compiling templates of tender documents and partial revision of templates; Finally, the knowledge of the new tender case was updated. The empirical analysis shows that the construction method of tender documents based on case-based reasoning not only proposes a suitable strategy for compiling tender documents, but also improves the compilation efficiency of tender documents

A Research on Dimension Reduction Method of Time Series Based on Trend Division

The characteristics of high dimension, complexity and multi granularity of financial time series make it difficult to deal with effectively. In order to solve the problem that the commonly used dimensionality reduction methods cannot reduce the dimensionality of time series with different granularity at the same time, in this paper, a method for dimensionality reduction of time series based on trend division is proposed. This method extracts the extreme value points of time series, identifies the important points in time series quickly and accurately, and compresses them. Experimental results show that, compared with the discrete Fourier transform and wavelet transform, the proposed method can effectively process data of different granularity and different trends on the basis of fully preserving the original information of time series. Moreover, the time complexity is low, the operation is easy, and the proposed method can provide decision support for high-frequency stock trading at the actual level

A Method for Filling up the Missed Data in Information Table

Almost algorithms based on the rough sets, such as mean value method, maximum frequency method, mode method are week in supporting the hidden rules in the information tablel. By the breaking point sets in the information system, a new method for packing the missed attribute value is provided in the paper,. The method is more efficient for indicating the decision rules

Data Mining In Oil Price Time Series Analysis

This paper sums up the applications of statistic models such as ARCH-family models, cointegration theory and Granger causality etc in oil price time series analysis and introduces the method of data mining combined with statistic knowledge to analysis oil price time series. In addition, the paper also explains advantages, functions, relevant technologies of this method and its potential applications in hedging the oil shock risk

Safety assessment of upper buried gas pipeline under blasting vibration of subway tunnel: a case study in Beijing subway line

As the buried gas pipelines are not easily field monitored, its status under the influence of tunnel blasting vibration is often difficult to obtain. In order to assess the safety of an upper buried gas pipeline under blasting vibration in the Beijing subway line 16, we established the 3D numerical model of the real field engineering to calculate and analyze the distribution of peak particle velocity (PPV) on the surface soil, proved its reliability by using the field monitoring data and discussed the dynamic response of the pipeline. Based on the analysis, we further assessed the safety of pipeline subjected to tunnel blasting vibration and concluded that the pipeline is only subjected to limited effect of tunnel blasting vibration and thus is safe