A new approach for generating efficient sample from market basket data

Classical data mining algorithms require expensive passes over the entire database to generate frequent items and hence to generate association rules. With the increase in the size of database, it is becoming very difficult to handle large amount of data for computation. One of the solutions to this problem is to generate sample from the database that acts as representative of the entire database for finding association rules in such a way that the distance of the sample from the complete database is minimal. Choosing correct sample that could represent data is not an easy task. Many algorithms have been proposed in the past. Some of them are computationally fast while others give better accuracy. In this paper, we present an algorithm for generating a sample from the database that can replace the entire database for generating association rules and is aimed at keeping a balance between accuracy and speed. The algorithm that is proposed takes into account the average number of small, medium and large 1-itemset in the database and average weight of the transactions to define threshold condition for the transactions. Set of transactions that satisfy the threshold condition is chosen as the representative for the entire database. The effectiveness of the proposed algorithm has been tested over several runs of database generated by IBM synthetic data generator. A vivid comparative performance evaluation of the proposed technique with the existing sampling techniques for comparing the accuracy and speed has also been carried out.     

Identifying some important success factors in adopting agile software development practices

Agile software development (ASD) is an emerging approach in software engineering, initially advocated by a group of 17 software professionals who practice a set of “lightweight” methods, and share a common set of values of software development. In this paper, we advance the state-of-the-art of the research in this area by conducting a survey-based ex-post-facto study for identifying factors from the perspective of the ASD practitioners that will influence the success of projects that adopt ASD practices. In this paper, we describe a hypothetical success factors framework we developed to address our research question, the hypotheses we conjectured, the research methodology, the data analysis techniques we used to validate the hypotheses, and the results we obtained from data analysis. The study was conducted using an unprecedentedly large-scale survey-based methodology, consisting of respondents who practice ASD and who had experience practicing plan-driven software development in the past. The study indicates that nine of the 14 hypothesized factors have statistically significant relationship with “Success”. The important success factors that were found are: customer satisfaction, customer collaboration, customer commitment, decision time, corporate culture, control, personal characteristics, societal culture, and training and learning.     

Speech enhancement using Teager energy operated ERB-like perceptual wavelet packet decomposition

In recent past, wavelet packet (WP) based speech enhancement techniques have been gaining popularity due to their inherent nature of noise minimization. WP based techniques appeared as more robust and efficient than short-time Fourier transform based methods. In the present work, a speech enhancement method using Teager energy operated equal rectangular bandwidth (ERB)-like WP decomposition has been proposed. Twenty four sub-band perceptual wavelet packet decomposition (PWPD) structure is implemented according to the auditory ERB scale. ERB scale based decomposition structure is used because the central frequency of the ERB scale distribution is similar to the frequency response of the human cochlea. Teager energy operator is applied to estimate the threshold value for the PWPD coefficients. Lastly, Wiener filtering is applied to remove the low frequency noise before final reconstruction stage. The proposed method has been applied to evaluate the Hindi sentences database, corrupted with six noise conditions. The proposed method’s performance is analysed with respect to several speech quality parameters and output signal to noise ratio levels. Performance indicates that the proposed technique outperforms some traditional speech enhancement algorithms at all SNR levels.     

Solving shortest path problem using particle swarm optimization

This paper presents the investigations on the application of particle swarm optimization (PSO) to solve shortest path (SP) routing problems. A modified priority-based encoding incorporating a heuristic operator for reducing the possibility of loop-formation in the path construction process is proposed for particle representation in PSO. Simulation experiments have been carried out on different network topologies for networks consisting of 15–70 nodes. It is noted that the proposed PSO-based approach can find the optimal path with good success rates and also can find closer sub-optimal paths with high certainty for all the tested networks. It is observed that the performance of the proposed algorithm surpasses those of recently reported genetic algorithm based approaches for this problem.     

Spatial interest pixels (SIPs): useful low-level features of visual media data

Visual media data such as an image is the raw data representation for many important applications. Reducing the dimensionality of raw visual media data is desirable since high dimensionality degrades not only the effectiveness but also the efficiency of visual recognition algorithms. We present a comparative study on spatial interest pixels (SIPs), including eight-way (a novel SIP detector), Harris, and Lucas‐Kanade, whose extraction is considered as an important step in reducing the dimensionality of visual media data. With extensive case studies, we have shown the usefulness of SIPs as low-level features of visual media data. A class-preserving dimension reduction algorithm (using GSVD) is applied to further reduce the dimension of feature vectors based on SIPs. The experiments showed its superiority over PCA.

Amounts of nondeterminism in finite automata

Summary The amount of nondeterminism in a nondeterministic finite automaton (NFA) is measured by counting the minimal number of guessing points a string w has to pass through on its way to an accepting state. NFA's with more nondeterminism can achieve greater savings in the number of states over their deterministic counterparts than NFA's with less nondeterminism. On the other hand, for some nontrivial infinite regular languages a deterministic finite automaton (DFA) can already be quite succinct in the sense that NFA's need as many states (and even context-free grammars need as many nonterminals) as the minimal DFA has states.This research was supported in part by the National Science Foundation under Grant No. MCS 76-10076     

Strict interpretations of deterministic pushdown acceptors

Three results are established. The first is that every nondeterministic strict interpretation of a deterministic pushdown acceptor (dpda) has an equivalent, deterministic, strict interpretation. The second is that ifM ₁ andM ₂ are two compatible strict interpretations of the dpdaM, then there exist deterministic strict interpretationsM andM such thatL(M ) =L(M ₁)L(M ₂) andL(M ) =L(M ₁)L(M ₂). The third states that there is no dpda whose strict interpretations yield all the deterministic context-free languages.This author was supported in part by the National Science Foundation under Grant MCS-77-22323.     

Formation and characterization of oxynitride glasses in the Si-Ca-Al-O-N and Si-Ca-Al,B-O-N systems

Oxynitride compositions in the Si-Ca-Al-O-N and Si-Ca-Al, B-O-N systems were melted and furnace-cooled in BN crucibles at temperatures from 1650 to 1850° C under dry nitrogen atmosphereS. Glass formation, phase stability and crystallization were studied by characterizing the cooled melts by X-ray diffraction, DTA, and electron microscopy. Oxynitride batches with nitrogen content up to 11 at % formed glasses in the Si-Ca-Al-O-N system. Glasses in the Si-Ca-Al, B-O-N system could be formed only when the B₂O₃ content of the batch was less than 3 wt %. Oxynitride glasses in these boron-containing systems were characteristically inhomogeneous, difficult to process, and prone to crystallization. In both the systems, glasses exhibited glass transitions beginning at 1000° C and crystallization at 1300 to 1500° C. Nitrogen-containing crystalline phases were identified in devitrified glasses via microstructural and micro-mechanical analyses.     

Microchemistry and microstructure of a multiphase aluminosilicate ceramic

The microstructure and microchemistry of a sintered (≈ 1700° C) aluminosilicate ceramic (60 wt % Al₂O₃-40 wt% SiO₂) was investigated by optical, scanning (SEM and EDAX), and analytical electron microscopy (TEM and STEM). The microstructural features of the fired ceramic consisted of unreacted Al₂O₃, glass, porosity, and equilibrium and metastable mullite phases. Residual Al₂O₃ agglomerates (≈ 15 to 30 μm in size) were surrounded by a ≈ 6 μm layer of equilibrium mullite (≈ 71.3 to 73.5 wt% Al₂O₃). The unreacted Al₂O₃-equilibrium mullite assembly formed islands embedded in a silica rich glass (≈ 4.5 to 14wt% Al₂O₃) which also contained 2 to 3 μm thick metastable mullite needles (≈ 70 to 77 wt% Al₂O₃). Phase separation and alumina rich glass compositions (≈ 57 to 59 wt% Al₂O₃) were also observed in some areas of the microstructure.